Merge pull request #258 from Kosinkadink/chore/bump-version-1.6.0

Bump version to 1.6.0

AGENTS.md

@@ -0,0 +1,272 @@
+# Advanced-ControlNet Contributor Guide
+
+This repository is expected to receive substantial AI-authored code. Treat this
+file as the implementation and verification contract for all changes, especially
+ports of new ControlNet families from ComfyUI.
+
+## Engineering Rules
+
+- Read the relevant ComfyUI implementation and this repository's equivalent
+  control path before editing. Do not design from a model card alone.
+- Make the smallest change that preserves vanilla ComfyUI behavior and adds the
+  established Advanced-ControlNet capabilities.
+- Reuse ComfyUI model classes, patchers, ops, model management, and loaders when
+  possible. Do not maintain a forked copy of core model code without a concrete
+  need.
+- Preserve existing node IDs, inputs, outputs, checkpoint locations, and saved
+  workflow compatibility. New node IDs generally use the `ACN_` prefix; keep
+  existing unprefixed IDs and the aliases in `nodes_deprecated.py` working.
+- Do not add dependencies unless the model cannot be supported with ComfyUI,
+  PyTorch, and the libraries already used by this repository.
+- Match the direct style of the surrounding file. Avoid one-use abstractions,
+  generic framework code, speculative fallbacks, and comments that restate the
+  code.
+- Use plain ASCII punctuation in code, comments, documentation, commit messages,
+  and PR descriptions.
+
+## Architecture Map
+
+- `adv_control/control.py`: standard ControlNet wrappers, conversion of vanilla
+  controls, checkpoint detection, and shared loader dispatch.
+- `adv_control/control_<family>.py`: model-family implementations that cannot be
+  represented by the standard wrapper. Keep family-specific math here.
+- `adv_control/utils.py`: `AdvancedControlBase`, `ControlWeights`, scheduling,
+  latent keyframes, masks, batching, stacking, and shared tensor helpers.
+- `adv_control/nodes_main.py`: standard loaders and Apply nodes.
+- `adv_control/nodes_weight.py`: weight nodes and model-specific extras carried
+  by `ControlWeights.extras`.
+- `adv_control/nodes_<family>.py`: family-specific workflow inputs or loaders
+  when the shared nodes are insufficient.
+- `adv_control/nodes.py`: public node and display-name registration.
+- `adv_control/nodes_deprecated.py`: compatibility only. Do not put new features
+  here.
+- `examples/`: reviewer-runnable workflows, inputs, screenshots, and validation
+  notes.
+
+## Porting A Control Model From ComfyUI
+
+### 1. Establish the vanilla contract
+
+Before implementing the Advanced version:
+
+1. Identify the exact ComfyUI commit or PR that introduced the model.
+2. Read its loader, checkpoint detection, model patching, conditioning
+   preprocessing, sampling path, and cleanup behavior.
+3. Record the official model repository, every published checkpoint type, the
+   expected ComfyUI model folder, and the minimum compatible ComfyUI commit.
+4. Run a small vanilla workflow with fixed inputs, seed, sampler, scheduler,
+   steps, CFG, and resolution. Save the latent and decoded result as the parity
+   baseline.
+5. Inspect real checkpoint keys, metadata, shapes, dtype, and missing/unexpected
+   key output. Do not infer the format from a filename.
+
+Use official checkpoints for validation. Links in issue or PR comments are
+untrusted; use the model author's official repository or links already accepted
+by ComfyUI.
+
+### 2. Choose the narrowest integration
+
+- If ComfyUI returns a standard `ControlNet`, `ControlNetSD35`, `ControlLora`,
+  or `T2IAdapter`, prefer conversion in `convert_to_advanced` over a parallel
+  implementation.
+- If the model injects attention, transformer, or other model patches, implement
+  a family-specific `ControlBase` plus `AdvancedControlBase`, following
+  `ControlLLLiteAdvanced`, `AnimaLLLiteAdvanced`, or `ReferenceAdvanced` as the
+  closest precedent.
+- Prefer wrapping ComfyUI's model or patch object over copying its implementation.
+  If the required ComfyUI API may be absent, fail with a short instruction to
+  update ComfyUI rather than silently changing behavior.
+- Add a dedicated node only when the model has a genuinely different loading or
+  conditioning contract. Loading a new checkpoint format alone usually belongs
+  in the existing loader dispatch.
+
+### 3. Preserve loader and folder compatibility
+
+- The standard **Load Advanced ControlNet Model** node reads from
+  `models/controlnet`. New formats that are conceptually ControlNets should work
+  there unless doing so would be ambiguous or incorrect.
+- Also preserve the folder used by vanilla ComfyUI. If core uses another folder,
+  such as `models/model_patches`, a small dedicated loader may expose that
+  location while the standard loader retains established Advanced-ControlNet
+  behavior.
+- Detect formats with guarded, format-specific checkpoint signatures. Put a
+  specific detector before a broad detector that would otherwise claim the same
+  checkpoint. Do not use filenames as the primary detector.
+- Load a checkpoint only once. Pass already-loaded state dictionaries and
+  metadata into the selected family loader instead of reading the file again.
+- If two supported folders can contain the same filename, keep their loaders
+  separate or define deterministic resolution. Never silently choose an
+  arbitrary duplicate.
+- Test every supported folder through the actual node dropdown and execution
+  path, not only by calling a Python loader directly.
+
+### 4. Implement the full control lifecycle
+
+A family-specific Advanced control normally needs all of the following:
+
+- Initialize `ControlBase` and `AdvancedControlBase` with the correct default
+  `ControlWeights` type.
+- Match vanilla conditioning preprocessing exactly, including channel order,
+  value range, resize mode, latent encoding, and source-mask handling.
+- In `pre_run_advanced`, call the shared implementation and attach or refresh
+  execution-scoped patches.
+- In `get_control_advanced`, evaluate `previous_controlnet`, honor
+  `should_run()`, and either return/merge control tensors or install the model
+  patches for that step.
+- Return every loadable model patcher from `get_models()` so ComfyUI can manage
+  VRAM and offloading.
+- Implement `copy()` using both ComfyUI's `copy_to()` and this repository's
+  `copy_to_advanced()`. Copies must not share mutable execution state that can
+  leak between conditioning branches or queued runs.
+- Clear prepared tensors, patch references, cached shapes, and other
+  execution-scoped state in `cleanup_advanced()`.
+- Use ComfyUI device, dtype, manual-cast, operations, and model-patcher APIs.
+  Do not hardcode CUDA, force float32, or move models manually when ComfyUI
+  already owns that lifecycle.
+- Preserve `previous_controlnet` behavior so same-family and mixed-family
+  controls can be stacked.
+
+## Advanced Feature Contract
+
+A port is not complete merely because default-strength generation works. Unless
+the model architecture makes a capability impossible, verify that it supports:
+
+- Apply-node strength and start/end percentage.
+- Timestep keyframes, including changing strength and inherited values.
+- Latent keyframes on a batch of at least two latents.
+- Apply-node effect masks and timestep-keyframe masks.
+- Default, universal/soft, and architecture-specific per-layer weights.
+- Weight overrides and model-specific weight extras.
+- Conditional/unconditional weighting when the selected weight node exposes it.
+- Stacking with another control, including correct `previous_controlnet` output.
+- Batched conditioning and sliding-context subset indexes where applicable.
+- Repeated execution, copying, cleanup, model offloading, and reload.
+
+Do not claim unsupported features in documentation. If an architecture cannot
+support a feature, document the reason and make incompatible weight types fail
+clearly through `compatible_weights`.
+
+### Masks and model-specific inputs
+
+- `mask_optional` on **Apply Advanced ControlNet** is always an effect mask. It
+  controls where this control influences generation.
+- A model's source mask, control-type selector, or other family-specific data is
+  not an effect mask. Do not overload `mask_optional` with a second meaning.
+- Do not add a model-specific input to the shared Apply node unless it is a
+  coherent capability needed by multiple model families.
+- Prefer a small family-specific extras node that stores auxiliary values in
+  `ControlWeights.extras`, then pass those weights through `weights_override`.
+  Define extras keys next to the model implementation rather than as unrelated
+  strings spread across nodes.
+- Validate required extras where they are first consumed and raise an actionable
+  error that names the exact nodes and connections needed to fix the workflow.
+- Apply effect masks at the actual injection representation. Attention-patch and
+  DiT controls may need token-space masks rather than the normal spatial control
+  tensor path.
+- Verify mask semantics with all-zero, all-one, and half-frame masks. All-zero
+  must equal no control and all-one must equal unmasked full control. Inspect the
+  multiplier at the injection site as well as the final image; global attention
+  can propagate influence outside directly controlled tokens.
+
+### Per-layer weights
+
+- Map custom weights to real architecture blocks in execution order. Confirm the
+  count from the loaded model, not from a model-card claim alone.
+- Default weights must reproduce vanilla output exactly.
+- Universal/soft weights must follow this repository's established progression
+  semantics. Implement a family-specific conversion only when the normal
+  `ControlWeights` layout does not represent the architecture.
+- Ordinary example workflows should use default weights. Do not connect an
+  advanced custom-weight node merely to demonstrate that it exists.
+
+## Required Validation
+
+Python import or compile checks are necessary but are not model validation. Use
+a real local ComfyUI installation, preferably managed by comfy-runner, with this
+repository linked as the custom node.
+
+### Vanilla parity
+
+For every published control type and materially different checkpoint format:
+
+1. Run vanilla ComfyUI and Advanced-ControlNet with identical model files,
+   conditioning, seed, sampler settings, and latent.
+2. Compare latent tensors before decode and decoded pixel arrays.
+3. Target maximum absolute latent difference `0.0` and identical pixels when
+   both paths implement the same math. If exact parity is impossible, explain
+   why and report a justified numerical tolerance plus image metrics.
+4. Confirm strength zero matches no control and strength one matches vanilla.
+5. Check logs for missing/unexpected keys, dtype/device errors, repeated model
+   loads, and cleanup failures.
+
+### Advanced behavior
+
+At minimum, execute focused workflows for:
+
+- A nontrivial start/end schedule or two timestep keyframes.
+- Batch size two with different latent-keyframe strengths.
+- All-zero, all-one, and half-frame effect masks.
+- Default weights and one nonuniform custom or soft-weight configuration.
+- Conditional/unconditional weighting when supported.
+- A stacked control path.
+- Missing required model-specific extras and the resulting readable error.
+- Both the vanilla model folder and any historical Advanced-ControlNet folder.
+- Re-queueing the same workflow to exercise copy and cleanup behavior.
+
+For model families with several control types, test every type. Do not assume
+that lineart, depth, pose, inpainting, union, or channel-count variants share the
+same conditioning contract.
+
+### Basic checks
+
+- Run `python -m compileall adv_control __init__.py` with the target ComfyUI
+  environment.
+- Parse every added workflow JSON.
+- Load each committed workflow in the real frontend, serialize it to an API
+  prompt, and execute that round-tripped prompt. This catches stale node IDs,
+  renamed inputs, invalid widgets, and missing model metadata.
+- Run `git diff --check`.
+
+There is currently no repository unit-test suite. Add focused tests when they
+can exercise pure detection, shape, mask, or scheduling logic without building
+a fake ComfyUI runtime. Do not add a large test framework solely for one port.
+
+## Examples and Review Evidence
+
+Every model-family port must be independently checkable by a reviewer:
+
+- Add one simple workflow for every public control type. Include required input
+  images or masks when licensing permits.
+- Keep ComfyUI's default node names. Use colored groups or regions to explain
+  branches; do not rename nodes, because reviewers need to identify their types.
+- Keep the normal workflows simple. Leave custom per-layer weights disconnected
+  unless a workflow specifically validates those advanced weights.
+- Include direct links to the official base model, encoder, VAE, and control
+  checkpoint repositories, plus the exact destination folder for each file.
+- Include a workflow screenshot and labeled output comparison in the PR. For
+  parity tests, show vanilla, Advanced-ControlNet, and an absolute-difference
+  result when practical.
+- Commit reusable workflows and small review images under `examples/<family>/`.
+  Do not commit model files, latent dumps, or large intermediate artifacts.
+- Document exact seeds/settings, expected numerical results, known limitations,
+  and reproduction steps in the example README and PR description.
+- If final-image interpretation is subtle, include the direct tensor-level
+  evidence needed to distinguish a real bug from model behavior.
+
+## Definition Of Done For A Model Port
+
+- [ ] The official checkpoint is detected without relying on its filename.
+- [ ] Vanilla and historical Advanced-ControlNet model folders are preserved.
+- [ ] Default output matches vanilla for every published control type.
+- [ ] Strength scheduling, keyframes, masks, weights, batching, and stacking are
+      tested or an architectural limitation is documented.
+- [ ] Effect masks are tested at the injection site and in decoded output.
+- [ ] Model-specific inputs use a narrow family boundary, not shared Apply-node
+      expansion.
+- [ ] `get_models`, `copy`, cleanup, dtype/device handling, and repeated runs are
+      verified.
+- [ ] Missing required inputs produce actionable errors.
+- [ ] Simple workflows, input assets, download links, screenshots, and exact
+      reproduction instructions are included.
+- [ ] Real frontend/API execution, compile checks, JSON parsing, and
+      `git diff --check` pass.

README.md

@@ -12,7 +12,7 @@ ControlNet preprocessors are available through [comfyui_controlnet_aux](https://
 - Replicate ***"ControlNet is more important"*** feature from sd-webui-controlnet extension via ***uncond_multiplier*** on ***Soft Weights***
   - uncond_multiplier=0.0 gives identical results of auto1111's feature, but values between 0.0 and 1.0 can be used without issue to granularly control the setting.
 - ControlNet, T2IAdapter, and ControlLoRA support for sliding context windows
-- ControlLLLite support
+- ControlLLLite support, including Anima LLLite v2 and inpainting models
 - ControlNet++ support
 - CtrLoRA support
   - Relevant models linked on [CtrLoRA github page](https://github.com/xyfJASON/ctrlora)
@@ -83,6 +83,12 @@ Loads a ControlNet model and converts it into an Advanced version that supports
 ### Outputs
 - 🟪***CONTROL_NET***: loaded Advanced ControlNet
 
+## Anima LLLite v2
+
+Place Anima LLLite v2 files in `ComfyUI/models/model_patches` and load them with **Load Anima LLLite Model**. The regular Advanced ControlNet loader also recognizes these models when they are placed in `ComfyUI/models/controlnet`.
+
+Use the loaded model with **Apply Advanced ControlNet**. For the 4-channel inpainting model, pass the source mask through **Anima LLLite Extras** into the `cn_extras` input of a weights node; `mask_optional` on the Apply node remains the Advanced-ControlNet effect mask. **ControlNet Custom Weights [Anima]** provides one weight for each of Anima's 28 transformer blocks. Timestep keyframes, latent keyframes, soft weights, CFG/unconditional weighting, effect masks, and stacked controls work the same as with other Advanced-ControlNet models.
+
 ## Timestep Keyframe
 ![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/404f3cfe-5852-4eed-935b-37e32493d1b5)
 

__init__.py

@@ -1,11 +1,10 @@
-from .adv_control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
-from .adv_control import documentation
+from .adv_control.nodes import AdvancedControlNetExtension
 from .adv_control.dinklink import init_dinklink
 from .adv_control.sampling import prepare_dinklink_acn_wrapper
 
-WEB_DIRECTORY = "./web"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]
-documentation.format_descriptions(NODE_CLASS_MAPPINGS)
-
 init_dinklink()
 prepare_dinklink_acn_wrapper()
+
+
+async def comfy_entrypoint() -> AdvancedControlNetExtension:
+    return AdvancedControlNetExtension()

adv_control/control.py

@@ -13,7 +13,7 @@ from comfy.controlnet import ControlBase, ControlNet, ControlNetSD35, ControlLor
 from comfy.model_patcher import ModelPatcher
 
 from .control_sparsectrl import SparseControlNet, SparseSettings, SparseConst, InterfaceAnimateDiffModel, create_sparse_modelpatcher, load_sparsectrl_motionmodel
-from .control_lllite import LLLiteModule, LLLitePatch, load_controllllite
+from .control_lllite import LLLiteModule, LLLitePatch, load_anima_lllite, load_controllllite
 from .control_svd import svd_unet_config_from_diffusers_unet, SVDControlNet, svd_unet_to_diffusers
 from .utils import (AdvancedControlBase, TimestepKeyframeGroup, LatentKeyframeGroup, AbstractPreprocWrapper, ControlWeightType, ControlWeights, WeightTypeException, Extras,
                     manual_cast_clean_groupnorm, disable_weight_init_clean_groupnorm, WrapperConsts, prepare_mask_batch, get_properly_arranged_t2i_weights, load_torch_file_with_dict_factory,
@@ -64,22 +64,22 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
 
         # make cond_hint appropriate dimensions
         # TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
-        if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * self.real_compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.real_compression_ratio != self.cond_hint.shape[3]:
+        if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[-2] * self.real_compression_ratio != self.cond_hint.shape[-2] or x_noisy.shape[-1] * self.real_compression_ratio != self.cond_hint.shape[-1]:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
             self.real_compression_ratio = self.compression_ratio
             compression_ratio = self.compression_ratio
             if self.vae is not None and self.mult_by_ratio_when_vae:
-                compression_ratio *= self.vae.downscale_ratio
+                compression_ratio *= self.vae.spacial_compression_encode()
             # if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
             if self.sub_idxs is not None:
                 actual_cond_hint_orig = self.cond_hint_original
                 if self.cond_hint_original.size(0) < self.full_latent_length:
                     actual_cond_hint_orig = extend_to_batch_size(tensor=actual_cond_hint_orig, batch_size=self.full_latent_length)
-                self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+                self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center")
             else:
-                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center")
             self.cond_hint = self.preprocess_image(self.cond_hint)
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -93,7 +93,10 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
                 to_concat = []
                 for c in self.extra_concat_orig:
                     c = c.to(self.cond_hint.device)
-                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, "center")
+                    if c.ndim < self.cond_hint.ndim:
+                        c = c.unsqueeze(2)
+                        c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2)
                     to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
                 self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
 
@@ -123,7 +126,7 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
         return super().pre_run_advanced(*args, **kwargs)
 
     def apply_advanced_strengths_and_masks(self, x: Tensor, batched_number: int, flux_shape=None):
-        if self.is_flux:
+        if self.is_flux or x.ndim == 3:
             flux_shape = self.x_noisy_shape
         return super().apply_advanced_strengths_and_masks(x, batched_number, flux_shape)
 
@@ -216,7 +219,7 @@ class T2IAdapterAdvanced(T2IAdapter, AdvancedControlBase):
                 del self.cond_hint
                 self.cond_hint = None
                 if full_cond_hint_original.size(0) < self.full_latent_length:
-                    actual_cond_hint_orig = extend_to_batch_size(tensor=full_cond_hint_original, batch_size=full_cond_hint_original.size(0))
+                    actual_cond_hint_orig = extend_to_batch_size(tensor=full_cond_hint_original, batch_size=self.full_latent_length)
                 self.cond_hint_original = actual_cond_hint_orig[self.sub_idxs]
             # mask hints
             self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
@@ -510,7 +513,7 @@ class SparseCtrlAdvanced(ControlNetAdvanced):
 
 
 def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, model=None):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    controlnet_data, metadata = comfy.utils.load_torch_file(ckpt_path, safe_load=True, return_metadata=True)
     # from pathlib import Path
     # log_name = ckpt_path.split('\\')[-1]
     # with open(Path(__file__).parent.parent.parent / rf"keys_{log_name}.txt", "w") as afile:
@@ -519,6 +522,10 @@ def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, mo
     control = None
     # check if a non-vanilla ControlNet
     controlnet_type = ControlWeightType.DEFAULT
+    is_anima_lllite = (
+        "lllite_conditioning1.conv1.weight" in controlnet_data
+        and any(key.startswith("lllite_dit_blocks_") for key in controlnet_data)
+    )
     has_controlnet_key = False
     has_motion_modules_key = False
     has_temporal_res_block_key = False
@@ -548,7 +555,9 @@ def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, mo
     elif has_controlnet_key and has_temporal_res_block_key:
         controlnet_type = ControlWeightType.SVD_CONTROLNET
 
-    if controlnet_type != ControlWeightType.DEFAULT:
+    if is_anima_lllite:
+        control = load_anima_lllite(ckpt_path, controlnet_data=controlnet_data, metadata=metadata, timestep_keyframe=timestep_keyframe)
+    elif controlnet_type != ControlWeightType.DEFAULT:
         if controlnet_type == ControlWeightType.CONTROLLLLITE:
             control = load_controllllite(ckpt_path, controlnet_data=controlnet_data, timestep_keyframe=timestep_keyframe)
         elif controlnet_type == ControlWeightType.SPARSECTRL:
@@ -804,13 +813,14 @@ def load_sparsectrl(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, tim
 
     if controlnet_config is None:
         unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
+        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_base_if_no_match=True).unet_config
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
         controlnet_config["operations"] = manual_cast_clean_groupnorm
     else:
         controlnet_config["operations"] = disable_weight_init_clean_groupnorm
+    controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     # get proper hint channels
     if use_simplified_conditioning_embedding:
@@ -845,6 +855,10 @@ def load_sparsectrl(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, tim
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
     if len(missing) > 0 or len(unexpected) > 0:
         logger.info(f"SparseCtrl ControlNet: {missing}, {unexpected}")
+    # cast control_model to the intended dtype; load_state_dict can leave weights
+    # in their on-disk dtype (e.g. comfy's lazy/zero-copy state dict loading), which
+    # would otherwise mismatch the activations at runtime
+    control_model = control_model.to(unet_dtype)
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -939,11 +953,12 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
 
     if controlnet_config is None:
         unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
+        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_base_if_no_match=True).unet_config
     load_device = comfy.model_management.get_torch_device()
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
         controlnet_config["operations"] = comfy.ops.manual_cast
+    controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = SVDControlNet(**controlnet_config)
@@ -972,6 +987,10 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
     if len(missing) > 0 or len(unexpected) > 0:
         logger.info(f"SVD-ControlNet: {missing}, {unexpected}")
+    # cast control_model to the intended dtype; load_state_dict can leave weights
+    # in their on-disk dtype (e.g. comfy's lazy/zero-copy state dict loading), which
+    # would otherwise mismatch the activations at runtime
+    control_model = control_model.to(unet_dtype)
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -980,4 +999,3 @@ def load_svdcontrolnet(ckpt_path: str, controlnet_data: dict[str, Tensor]=None,
 
     control = SVDControlNetAdvanced(control_model, timestep_keyframes=timestep_keyframe, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
-

adv_control/control_lllite.py

@@ -10,14 +10,24 @@ import os
 import comfy.utils
 import comfy.ops
 import comfy.model_management
+import comfy.model_patcher
 from comfy.model_patcher import ModelPatcher
 from comfy.controlnet import ControlBase
 
+try:
+    import comfy.ldm.anima.lllite as comfy_anima_lllite
+except ImportError:
+    comfy_anima_lllite = None
+
 from .logger import logger
 from .utils import (AdvancedControlBase, TimestepKeyframeGroup, ControlWeights, broadcast_image_to_extend, extend_to_batch_size,
                     prepare_mask_batch)
 
 
+class AnimaLLLiteConst:
+    INPAINT_MASK = "anima_lllite_inpaint_mask"
+
+
 # based on set_model_patch code in comfy/model_patcher.py
 def set_model_patch(transformer_options, patch, name):
     to = transformer_options
@@ -223,7 +233,7 @@ class LLLiteModule(torch.nn.Module):
                 mask = prepare_mask_batch(control.mask_cond_hint, (1, 1, h, w)).to(cx.dtype)
                 mask = mask.view(mask.shape[0], 1, h * w).permute(0, 2, 1)
             if control.tk_mask_cond_hint is not None:
-                mask_tk = prepare_mask_batch(control.mask_cond_hint, (1, 1, h, w)).to(cx.dtype)
+                mask_tk = prepare_mask_batch(control.tk_mask_cond_hint, (1, 1, h, w)).to(cx.dtype)
                 mask_tk = mask_tk.view(mask_tk.shape[0], 1, h * w).permute(0, 2, 1)
 
         # x in uncond/cond doubles batch size
@@ -240,7 +250,7 @@ class LLLiteModule(torch.nn.Module):
 
         if mask is None:
             mask = 1.0
-        elif mask_tk is not None:
+        if mask_tk is not None:
             mask = mask * mask_tk
 
         #logger.info(f"cs: {cx.shape}, x: {x.shape}, is_conv2d: {self.is_conv2d}")
@@ -250,7 +260,7 @@ class LLLiteModule(torch.nn.Module):
         if control.latent_keyframes is not None:
             cx = cx * control.calc_latent_keyframe_mults(x=cx, batched_number=control.batched_number)
         if control.weights is not None and control.weights.has_uncond_multiplier:
-            cond_or_uncond = control.batched_number.cond_or_uncond
+            cond_or_uncond = control.cond_or_uncond
             actual_length = cx.size(0) // control.batched_number
             for idx, cond_type in enumerate(cond_or_uncond):
                 # if uncond, set to weight's uncond_multiplier
@@ -371,6 +381,233 @@ class ControlLLLiteAdvanced(ControlBase, AdvancedControlBase):
         return c
 
 
+class AnimaLLLiteAdvancedPatch:
+    def __init__(self, model_patch, control: 'AnimaLLLiteAdvanced'=None):
+        self.model_patch = model_patch
+        self.control = control
+
+    def set_control(self, control: 'AnimaLLLiteAdvanced') -> 'AnimaLLLiteAdvancedPatch':
+        self.control = control
+        return self
+
+    def clone_with_control(self, control: 'AnimaLLLiteAdvanced') -> 'AnimaLLLiteAdvancedPatch':
+        return AnimaLLLiteAdvancedPatch(self.model_patch, control)
+
+    def __call__(self, args):
+        if not self.control.should_run():
+            return args
+
+        x = args["x"]
+        if x.shape[2] != 1:
+            raise ValueError(f"Anima LLLite only supports T=1, got T={x.shape[2]}")
+
+        target_height = x.shape[-2] * 8
+        target_width = x.shape[-1] * 8
+        image = self.control.prepare_batched_tensor(self.control.cond_hint_original, x.shape[0])[:, :3]
+        image = comfy.utils.common_upscale(image, target_width, target_height, "bicubic", crop="center").clamp(0.0, 1.0)
+        image = image.to(device=x.device, dtype=x.dtype) * 2.0 - 1.0
+
+        if self.model_patch.model.cond_in_channels == 4:
+            mask = self.control.weights.extras.get(AnimaLLLiteConst.INPAINT_MASK)
+            if mask is None:
+                raise ValueError(
+                    "Anima LLLite inpainting models require an inpaint mask. Connect a MASK to Anima LLLite Extras, "
+                    "connect its cn_extras output to Default Weights, then connect CN_WEIGHTS to weights_override on Apply Advanced ControlNet."
+                )
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+            if mask.ndim != 4 or mask.shape[1] != 1:
+                raise ValueError(f"Anima LLLite mask must have one channel, got shape {tuple(mask.shape)}")
+            if image.shape[0] > 1:
+                mask = self.control.prepare_batched_tensor(mask, image.shape[0], except_one=False)
+            mask = comfy.utils.common_upscale(mask.float(), target_width, target_height, "nearest-exact", crop="center")
+            if mask.shape[0] != image.shape[0]:
+                if image.shape[0] % mask.shape[0] != 0:
+                    raise ValueError(f"Anima LLLite mask batch {mask.shape[0]} cannot be broadcast to image batch {image.shape[0]}")
+                mask = mask.repeat(image.shape[0] // mask.shape[0], 1, 1, 1)
+            mask = (mask >= 0.5).to(device=x.device, dtype=x.dtype)
+            if self.model_patch.model.inpaint_masked_input:
+                image = image * (mask < 0.5).to(image.dtype)
+            image = torch.cat((image, mask * 2.0 - 1.0), dim=1)
+
+        cond_emb = self.model_patch.model.encode_conditioning(image)
+        multiplier, weight_mask = self.control.prepare_multiplier(args["img"])
+        args["transformer_options"]["model_patch_data"][self] = (cond_emb, multiplier, weight_mask)
+        return args
+
+    def to(self, device_or_dtype):
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
+
+class AnimaLLLiteAdvancedAttentionPatch:
+    def __init__(self, patch: AnimaLLLiteAdvancedPatch, targets):
+        self.patch = patch
+        self.targets = targets
+
+    def __call__(self, q, k, v, pe=None, attn_mask=None, extra_options=None):
+        patch_data = extra_options["model_patch_data"].get(self.patch)
+        if patch_data is None:
+            return {"q": q, "k": k, "v": v, "pe": pe, "attn_mask": attn_mask}
+
+        cond_emb, multiplier, weight_mask = patch_data
+        block_index = extra_options["block_index"]
+        strength = self.patch.control.get_block_strength(block_index, multiplier, weight_mask)
+        values = {"q": q, "k": k, "v": v}
+        for value_name, target in self.targets.items():
+            values[value_name] = self.patch.model_patch.model.apply(values[value_name], cond_emb, block_index, target, strength)
+        return {"q": values["q"], "k": values["k"], "v": values["v"], "pe": pe, "attn_mask": attn_mask}
+
+
+class AnimaLLLiteAdvancedMLPPatch:
+    def __init__(self, patch: AnimaLLLiteAdvancedPatch):
+        self.patch = patch
+
+    def __call__(self, args):
+        patch_data = args["transformer_options"]["model_patch_data"].get(self.patch)
+        if patch_data is None:
+            return args
+
+        cond_emb, multiplier, weight_mask = patch_data
+        block_index = args["transformer_options"]["block_index"]
+        strength = self.patch.control.get_block_strength(block_index, multiplier, weight_mask)
+        args["x"] = self.patch.model_patch.model.apply(args["x"], cond_emb, block_index, "mlp_layer1", strength)
+        return args
+
+
+class AnimaLLLiteAdvanced(ControlBase, AdvancedControlBase):
+    def __init__(self, model_patch, timestep_keyframes: TimestepKeyframeGroup):
+        ControlBase.__init__(self)
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite())
+        self.model_patch = model_patch
+        self.patch = AnimaLLLiteAdvancedPatch(model_patch, self)
+        self.patch_attn1 = AnimaLLLiteAdvancedAttentionPatch(
+            self.patch,
+            {"q": "self_attn_q_proj", "k": "self_attn_k_proj", "v": "self_attn_v_proj"},
+        )
+        self.patch_attn2 = AnimaLLLiteAdvancedAttentionPatch(self.patch, {"q": "cross_attn_q_proj"})
+        self.patch_mlp = AnimaLLLiteAdvancedMLPPatch(self.patch)
+
+    def prepare_batched_tensor(self, tensor: Tensor, target_batch: int, except_one=True) -> Tensor:
+        if self.sub_idxs is not None:
+            if tensor.shape[0] < self.full_latent_length:
+                tensor = extend_to_batch_size(tensor, self.full_latent_length)
+            tensor = tensor[self.sub_idxs]
+        if tensor.shape[0] != target_batch:
+            tensor = broadcast_image_to_extend(tensor, target_batch, self.batched_number, except_one=except_one)
+        return tensor
+
+    def prepare_effect_mask(self, mask: Tensor, img: Tensor) -> Tensor:
+        mask = self.prepare_batched_tensor(mask, img.shape[0], except_one=False)
+        mask = torch.nn.functional.interpolate(
+            mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).float(),
+            size=(img.shape[2], img.shape[3]),
+            mode="bilinear",
+        )
+        return mask.flatten(2).transpose(1, 2).to(device=img.device, dtype=img.dtype)
+
+    def prepare_multiplier(self, img: Tensor):
+        multiplier = self.strength * self._current_timestep_keyframe.strength
+        token_multiplier = None
+
+        masks = [self.mask_cond_hint_original, self._current_timestep_keyframe.mask_hint_orig]
+        for mask in masks:
+            if mask is not None:
+                mask_multiplier = self.prepare_effect_mask(mask, img)
+                token_multiplier = mask_multiplier if token_multiplier is None else token_multiplier * mask_multiplier
+
+        flat_img = img.flatten(1, 3)
+        if self.latent_keyframes is not None:
+            latent_multiplier = self.calc_latent_keyframe_mults(flat_img, self.batched_number)
+            token_multiplier = latent_multiplier if token_multiplier is None else token_multiplier * latent_multiplier
+
+        if self.weights.has_uncond_multiplier and self.cond_or_uncond is not None:
+            batch_multiplier = torch.ones((img.shape[0], 1, 1), dtype=img.dtype, device=img.device)
+            actual_length = img.shape[0] // self.batched_number
+            for idx, cond_type in enumerate(self.cond_or_uncond):
+                if cond_type == 1:
+                    batch_multiplier[actual_length * idx:actual_length * (idx + 1)] *= self.weights.uncond_multiplier
+            token_multiplier = batch_multiplier if token_multiplier is None else token_multiplier * batch_multiplier
+
+        weight_mask = None
+        if self.weights.weight_mask is not None:
+            weight_mask = self.prepare_effect_mask(self.weights.weight_mask, img)
+
+        if token_multiplier is not None:
+            multiplier = token_multiplier * multiplier
+        return multiplier, weight_mask
+
+    def get_block_strength(self, block_index: int, multiplier, weight_mask):
+        block_weight = 1.0
+        if self.weights.weight_type == "universal":
+            exponent = self.model_patch.model.block_count - block_index
+            if weight_mask is not None:
+                block_weight = torch.pow(weight_mask, exponent)
+            else:
+                block_weight = self.weights.base_multiplier ** exponent
+        elif self.weights.weights_input is not None and block_index < len(self.weights.weights_input):
+            block_weight = self.weights.weights_input[block_index]
+        return multiplier * block_weight
+
+    def pre_run_advanced(self, *args, **kwargs):
+        AdvancedControlBase.pre_run_advanced(self, *args, **kwargs)
+        self.patch.set_control(self)
+
+    def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int, transformer_options: dict):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options)
+        if not self.should_run():
+            return control_prev
+
+        set_model_patch(transformer_options, self.patch.set_control(self), "post_input")
+        set_model_attn1_patch(transformer_options, self.patch_attn1)
+        set_model_attn2_patch(transformer_options, self.patch_attn2)
+        set_model_patch(transformer_options, self.patch_mlp, "mlp_patch")
+        return control_prev
+
+    def get_models(self):
+        models = super().get_models()
+        models.append(self.model_patch)
+        return models
+
+    def copy(self):
+        copied = AnimaLLLiteAdvanced(self.model_patch, self.timestep_keyframes)
+        self.copy_to(copied)
+        self.copy_to_advanced(copied)
+        return copied
+
+
+def load_anima_lllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, metadata=None, timestep_keyframe: TimestepKeyframeGroup=None):
+    if comfy_anima_lllite is None:
+        raise RuntimeError("Anima LLLite requires a newer version of ComfyUI. Please update ComfyUI.")
+    if controlnet_data is None or metadata is None:
+        loaded_data, loaded_metadata = comfy.utils.load_torch_file(ckpt_path, safe_load=True, return_metadata=True)
+        if controlnet_data is None:
+            controlnet_data = loaded_data
+        metadata = loaded_metadata
+
+    dtype = comfy.utils.weight_dtype(controlnet_data)
+    model = comfy_anima_lllite.AnimaLLLite(
+        controlnet_data,
+        metadata,
+        device=comfy.model_management.unet_offload_device(),
+        dtype=dtype,
+        operations=comfy.ops.manual_cast,
+    )
+    patcher_type = getattr(comfy.model_patcher, "CoreModelPatcher", ModelPatcher)
+    model_patcher = patcher_type(
+        model,
+        load_device=comfy.model_management.get_torch_device(),
+        offload_device=comfy.model_management.unet_offload_device(),
+    )
+    is_dynamic = getattr(model_patcher, "is_dynamic", lambda: False)()
+    model.load_state_dict(controlnet_data, assign=is_dynamic)
+    return AnimaLLLiteAdvanced(model_patcher, timestep_keyframe)
+
+
 def load_controllllite(ckpt_path: str, controlnet_data: dict[str, Tensor]=None, timestep_keyframe: TimestepKeyframeGroup=None):
     if controlnet_data is None:
         controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)

adv_control/control_reference.py

@@ -773,6 +773,9 @@ def refcn_diffusion_model_wrapper_factory(reference_injections: ReferenceInjecti
         # if nothing related to reference controlnets, do nothing special
         if len(ref_controlnets) == 0 and len(context_controlnets) == 0:
             return executor(x, *args, **kwargs)
+        adain_controlnets = []
+        context_adain_controlnets = []
+        orig_forward_timestep_embed = None
         try:
             # assign cond and uncond idxs
             batched_number = len(transformer_options["cond_or_uncond"])
@@ -784,14 +787,12 @@ def refcn_diffusion_model_wrapper_factory(reference_injections: ReferenceInjecti
             transformer_options[REF_COND_IDXS] = [i for i, z in enumerate(indiv_conds) if z == 0]
             # check which controlnets do which thing
             attn_controlnets = []
-            adain_controlnets = []
             for control in ref_controlnets:
                 if ReferenceType.is_attn(control.ref_opts.reference_type):
                     attn_controlnets.append(control)
                 if ReferenceType.is_adain(control.ref_opts.reference_type):
                     adain_controlnets.append(control)
             context_attn_controlnets = []
-            context_adain_controlnets = []
             # for ease of access, store current contextref_cond_idx value
             if len(context_controlnets) == 0:
                 transformer_options[CONTEXTREF_TEMP_COND_IDX] = -1
@@ -877,7 +878,7 @@ def refcn_diffusion_model_wrapper_factory(reference_injections: ReferenceInjecti
         finally:
             # make sure ref banks are cleared no matter what happens - otherwise, RIP VRAM
             reference_injections.clean_ref_module_mem()
-            if len(adain_controlnets) > 0 or len(context_adain_controlnets) > 0:
+            if orig_forward_timestep_embed is not None:
                 openaimodel.forward_timestep_embed = orig_forward_timestep_embed
     return refcn_diffusion_model_wrapper
 

adv_control/documentation.py

@@ -1,47 +0,0 @@
-from .logger import logger
-
-def image(src):
-    return f'<img src={src} style="width: 0px; min-width: 100%">'
-def video(src):
-    return f'<video src={src} autoplay muted loop controls controlslist="nodownload noremoteplayback noplaybackrate" style="width: 0px; min-width: 100%" class="VHS_loopedvideo">'
-def short_desc(desc):
-    return  f'<div id=VHS_shortdesc style="font-size: .8em">{desc}</div>'
-
-descriptions = {
-}
-
-sizes = ['1.4','1.2','1']
-def as_html(entry, depth=0):
-    if isinstance(entry, dict):
-        size = 0.8 if depth < 2 else 1
-        html = ''
-        for k in entry:
-            if k == "collapsed":
-                continue
-            collapse_single = k.endswith("_collapsed")
-            if collapse_single:
-                name = k[:-len("_collapsed")]
-            else:
-                name = k
-            collapse_flag = ' VHS_precollapse' if entry.get("collapsed", False) or collapse_single else ''
-            html += f'<div vhs_title=\"{name}\" style=\"display: flex; font-size: {size}em\" class=\"VHS_collapse{collapse_flag}\"><div style=\"color: #AAA; height: 1.5em;\">[<span style=\"font-family: monospace\">-</span>]</div><div style=\"width: 100%\">{name}: {as_html(entry[k], depth=depth+1)}</div></div>'
-        return html
-    if isinstance(entry, list):
-        html = ''
-        for i in entry:
-            html += f'<div>{as_html(i, depth=depth)}</div>'
-        return html
-    return str(entry)
-
-def format_descriptions(nodes):
-    for k in descriptions:
-        if k.endswith("_collapsed"):
-            k = k[:-len("_collapsed")]
-        nodes[k].DESCRIPTION = as_html(descriptions[k])
-    # undocumented_nodes = []
-    # for k in nodes:
-    #     if not hasattr(nodes[k], "DESCRIPTION"):
-    #         undocumented_nodes.append(k)
-    # if len(undocumented_nodes) > 0:
-    #     logger.info(f"Undocumented nodes: {undocumented_nodes}")
-

adv_control/nodes.py

@@ -1,136 +1,73 @@
-import comfy.sample
+from comfy_api.latest import ComfyExtension, io
 
-from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced,
+from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced, AnimaLLLiteLoaderAdvanced,
                          AdvancedControlNetApply, AdvancedControlNetApplySingle)
 from .nodes_weight import (DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights,
                            SoftControlNetWeightsSD15, CustomControlNetWeightsSD15, CustomControlNetWeightsFlux,
-                           SoftT2IAdapterWeights, CustomT2IAdapterWeights, ExtrasMiddleMultNode)
+                           CustomControlNetWeightsAnima, SoftT2IAdapterWeights, CustomT2IAdapterWeights, ExtrasMiddleMultNode,
+                           AnimaLLLiteExtras)
 from .nodes_keyframes import (LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode,
                               TimestepKeyframeNode, TimestepKeyframeInterpolationNode, TimestepKeyframeFromStrengthListNode)
 from .nodes_sparsectrl import SparseCtrlMergedLoaderAdvanced, SparseCtrlLoaderAdvanced, SparseIndexMethodNode, SparseSpreadMethodNode, RgbSparseCtrlPreprocessor, SparseWeightExtras
 from .nodes_reference import ReferenceControlNetNode, ReferenceControlFinetune, ReferencePreprocessorNode
 from .nodes_plusplus import PlusPlusLoaderAdvanced, PlusPlusLoaderSingle, PlusPlusInputNode
 from .nodes_ctrlora import CtrLoRALoader
-from .nodes_loosecontrol import ControlNetLoaderWithLoraAdvanced
 from .nodes_deprecated import (LoadImagesFromDirectory, ScaledSoftUniversalWeightsDeprecated,
                                SoftControlNetWeightsDeprecated, CustomControlNetWeightsDeprecated, 
                                SoftT2IAdapterWeightsDeprecated, CustomT2IAdapterWeightsDeprecated,
                                AdvancedControlNetApplyDEPR, AdvancedControlNetApplySingleDEPR,
                                ControlNetLoaderAdvancedDEPR, DiffControlNetLoaderAdvancedDEPR)
-from .logger import logger
 
 
-# NODE MAPPING
-NODE_CLASS_MAPPINGS = {
-    # Keyframes
-    "TimestepKeyframe": TimestepKeyframeNode,
-    "ACN_TimestepKeyframeInterpolation": TimestepKeyframeInterpolationNode,
-    "ACN_TimestepKeyframeFromStrengthList": TimestepKeyframeFromStrengthListNode,
-    "LatentKeyframe": LatentKeyframeNode,
-    "LatentKeyframeTiming": LatentKeyframeInterpolationNode,
-    "LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
-    "LatentKeyframeGroup": LatentKeyframeGroupNode,
-    # Conditioning
-    "ACN_AdvancedControlNetApply_v2": AdvancedControlNetApply,
-    "ACN_AdvancedControlNetApplySingle_v2": AdvancedControlNetApplySingle,
-    # Loaders
-    "ACN_ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
-    "ACN_DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
-    # Weights
-    "ACN_ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
-    "ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
-    "ACN_SoftControlNetWeightsSD15": SoftControlNetWeightsSD15,
-    "ACN_CustomControlNetWeightsSD15": CustomControlNetWeightsSD15,
-    "ACN_CustomControlNetWeightsFlux": CustomControlNetWeightsFlux,
-    "ACN_SoftT2IAdapterWeights": SoftT2IAdapterWeights,
-    "ACN_CustomT2IAdapterWeights": CustomT2IAdapterWeights,
-    "ACN_DefaultUniversalWeights": DefaultWeights,
-    "ACN_ExtrasMiddleMult": ExtrasMiddleMultNode,
-    # SparseCtrl
-    "ACN_SparseCtrlRGBPreprocessor": RgbSparseCtrlPreprocessor,
-    "ACN_SparseCtrlLoaderAdvanced": SparseCtrlLoaderAdvanced,
-    "ACN_SparseCtrlMergedLoaderAdvanced": SparseCtrlMergedLoaderAdvanced,
-    "ACN_SparseCtrlIndexMethodNode": SparseIndexMethodNode,
-    "ACN_SparseCtrlSpreadMethodNode": SparseSpreadMethodNode,
-    "ACN_SparseCtrlWeightExtras": SparseWeightExtras,
-    # ControlNet++
-    "ACN_ControlNet++LoaderSingle": PlusPlusLoaderSingle,
-    "ACN_ControlNet++LoaderAdvanced": PlusPlusLoaderAdvanced,
-    "ACN_ControlNet++InputNode": PlusPlusInputNode,
-    # CtrLoRA
-    "ACN_CtrLoRALoader": CtrLoRALoader,
-    # Reference
-    "ACN_ReferencePreprocessor": ReferencePreprocessorNode,
-    "ACN_ReferenceControlNet": ReferenceControlNetNode,
-    "ACN_ReferenceControlNetFinetune": ReferenceControlFinetune,
-    # LOOSEControl
-    #"ACN_ControlNetLoaderWithLoraAdvanced": ControlNetLoaderWithLoraAdvanced,
-    # Deprecated
-    "LoadImagesFromDirectory": LoadImagesFromDirectory,
-    "ScaledSoftControlNetWeights": ScaledSoftUniversalWeightsDeprecated,
-    "SoftControlNetWeights": SoftControlNetWeightsDeprecated,
-    "CustomControlNetWeights": CustomControlNetWeightsDeprecated,
-    "SoftT2IAdapterWeights": SoftT2IAdapterWeightsDeprecated,
-    "CustomT2IAdapterWeights": CustomT2IAdapterWeightsDeprecated,
-    "ACN_AdvancedControlNetApply": AdvancedControlNetApplyDEPR,
-    "ACN_AdvancedControlNetApplySingle": AdvancedControlNetApplySingleDEPR,
-    "ControlNetLoaderAdvanced": ControlNetLoaderAdvancedDEPR,
-    "DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvancedDEPR,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    # Keyframes
-    "TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
-    "ACN_TimestepKeyframeInterpolation": "Timestep Keyframe Interp. 🛂🅐🅒🅝",
-    "ACN_TimestepKeyframeFromStrengthList": "Timestep Keyframe From List 🛂🅐🅒🅝",
-    "LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
-    "LatentKeyframeTiming": "Latent Keyframe Interp. 🛂🅐🅒🅝",
-    "LatentKeyframeBatchedGroup": "Latent Keyframe From List 🛂🅐🅒🅝",
-    "LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
-    # Conditioning
-    "ACN_AdvancedControlNetApply_v2": "Apply Advanced ControlNet 🛂🅐🅒🅝",
-    "ACN_AdvancedControlNetApplySingle_v2": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
-    # Loaders
-    "ACN_ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
-    "ACN_DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
-    # Weights
-    "ACN_ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
-    "ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
-    "ACN_SoftControlNetWeightsSD15": "ControlNet Soft Weights [SD1.5] 🛂🅐🅒🅝",
-    "ACN_CustomControlNetWeightsSD15": "ControlNet Custom Weights [SD1.5] 🛂🅐🅒🅝",
-    "ACN_CustomControlNetWeightsFlux": "ControlNet Custom Weights [Flux] 🛂🅐🅒🅝",
-    "ACN_SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
-    "ACN_CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
-    "ACN_DefaultUniversalWeights": "Default Weights 🛂🅐🅒🅝",
-    "ACN_ExtrasMiddleMult": "Middle Weight Extras 🛂🅐🅒🅝",
-    # SparseCtrl
-    "ACN_SparseCtrlRGBPreprocessor": "RGB SparseCtrl 🛂🅐🅒🅝",
-    "ACN_SparseCtrlLoaderAdvanced": "Load SparseCtrl Model 🛂🅐🅒🅝",
-    "ACN_SparseCtrlMergedLoaderAdvanced": "🧪Load Merged SparseCtrl Model 🛂🅐🅒🅝",
-    "ACN_SparseCtrlIndexMethodNode": "SparseCtrl Index Method 🛂🅐🅒🅝",
-    "ACN_SparseCtrlSpreadMethodNode": "SparseCtrl Spread Method 🛂🅐🅒🅝",
-    "ACN_SparseCtrlWeightExtras": "SparseCtrl Weight Extras 🛂🅐🅒🅝",
-    # ControlNet++
-    "ACN_ControlNet++LoaderSingle": "Load ControlNet++ Model (Single) 🛂🅐🅒🅝",
-    "ACN_ControlNet++LoaderAdvanced": "Load ControlNet++ Model (Multi) 🛂🅐🅒🅝",
-    "ACN_ControlNet++InputNode": "ControlNet++ Input 🛂🅐🅒🅝",
-    # CtrLoRA
-    "ACN_CtrLoRALoader": "Load CtrLoRA Model 🛂🅐🅒🅝",
-    # Reference
-    "ACN_ReferencePreprocessor": "Reference Preproccessor 🛂🅐🅒🅝",
-    "ACN_ReferenceControlNet": "Reference ControlNet 🛂🅐🅒🅝",
-    "ACN_ReferenceControlNetFinetune": "Reference ControlNet (Finetune) 🛂🅐🅒🅝",
-    # LOOSEControl
-    #"ACN_ControlNetLoaderWithLoraAdvanced": "Load Adv. ControlNet Model w/ LoRA 🛂🅐🅒🅝",
-    # Deprecated
-    "LoadImagesFromDirectory": "🚫Load Images [DEPRECATED] 🛂🅐🅒🅝",
-    "ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
-    "SoftControlNetWeights": "ControlNet Soft Weights 🛂🅐🅒🅝",
-    "CustomControlNetWeights": "ControlNet Custom Weights 🛂🅐🅒🅝",
-    "SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
-    "CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
-    "ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
-    "ACN_AdvancedControlNetApplySingle": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
-    "ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
-    "DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
-}
+
+class AdvancedControlNetExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TimestepKeyframeNode,
+            TimestepKeyframeInterpolationNode,
+            TimestepKeyframeFromStrengthListNode,
+            LatentKeyframeNode,
+            LatentKeyframeInterpolationNode,
+            LatentKeyframeBatchedGroupNode,
+            LatentKeyframeGroupNode,
+            AdvancedControlNetApply,
+            AdvancedControlNetApplySingle,
+            ControlNetLoaderAdvanced,
+            DiffControlNetLoaderAdvanced,
+            AnimaLLLiteLoaderAdvanced,
+            ScaledSoftUniversalWeights,
+            ScaledSoftMaskedUniversalWeights,
+            SoftControlNetWeightsSD15,
+            CustomControlNetWeightsSD15,
+            CustomControlNetWeightsFlux,
+            CustomControlNetWeightsAnima,
+            SoftT2IAdapterWeights,
+            CustomT2IAdapterWeights,
+            DefaultWeights,
+            ExtrasMiddleMultNode,
+            AnimaLLLiteExtras,
+            RgbSparseCtrlPreprocessor,
+            SparseCtrlLoaderAdvanced,
+            SparseCtrlMergedLoaderAdvanced,
+            SparseIndexMethodNode,
+            SparseSpreadMethodNode,
+            SparseWeightExtras,
+            PlusPlusLoaderSingle,
+            PlusPlusLoaderAdvanced,
+            PlusPlusInputNode,
+            CtrLoRALoader,
+            ReferencePreprocessorNode,
+            ReferenceControlNetNode,
+            ReferenceControlFinetune,
+            LoadImagesFromDirectory,
+            ScaledSoftUniversalWeightsDeprecated,
+            SoftControlNetWeightsDeprecated,
+            CustomControlNetWeightsDeprecated,
+            SoftT2IAdapterWeightsDeprecated,
+            CustomT2IAdapterWeightsDeprecated,
+            AdvancedControlNetApplyDEPR,
+            AdvancedControlNetApplySingleDEPR,
+            ControlNetLoaderAdvancedDEPR,
+            DiffControlNetLoaderAdvancedDEPR
+        ]

adv_control/nodes_ctrlora.py

@@ -1,25 +1,28 @@
+from comfy_api.latest import io
 import folder_paths
 
 from .control_ctrlora import load_ctrlora
 
-
-class CtrLoRALoader:
+class CtrLoRALoader(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "base": (folder_paths.get_filename_list("controlnet"), ),
-                "lora": (folder_paths.get_filename_list("controlnet"), ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_CtrLoRALoader',
+            display_name='Load CtrLoRA Model 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/CtrLoRA',
+            inputs=[
+                io.Combo.Input('base', options=folder_paths.get_filename_list("controlnet")),
+                io.Combo.Input('lora', options=folder_paths.get_filename_list("controlnet"))
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/CtrLoRA"
-
-    def load_controlnet_plusplus(self, base: str, lora: str):
+    @classmethod
+    def execute(cls, base: str, lora: str):
         base_path = folder_paths.get_full_path("controlnet", base)
         lora_path = folder_paths.get_full_path("controlnet", lora)
         controlnet = load_ctrlora(base_path, lora_path)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)

adv_control/nodes_deprecated.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 import os
 
 import torch
@@ -7,29 +8,31 @@ import numpy as np
 from PIL import Image, ImageOps
 from .control import load_controlnet, is_advanced_controlnet
 from .nodes_main import AdvancedControlNetApply
-from .utils import BIGMAX, ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
-from .logger import logger
+from .utils import ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
 
-
-class LoadImagesFromDirectory:
+class LoadImagesFromDirectory(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "directory": ("STRING", {"default": ""}),
-            },
-            "optional": {
-                "image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                "start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='LoadImagesFromDirectory',
+            display_name='🚫Load Images [DEPRECATED] 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.String.Input('directory', default=''),
+                io.Int.Input('image_load_cap', optional=True, default=0, max=9007199254740991, min=0, step=1),
+                io.Int.Input('start_index', optional=True, default=0, max=9007199254740991, min=0, step=1)
+            ],
+            outputs=[
+                io.Image.Output('IMAGE', is_output_list=False),
+                io.Mask.Output('MASK', is_output_list=False),
+                io.Int.Output('INT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    RETURN_TYPES = ("IMAGE", "MASK", "INT")
-    FUNCTION = "load_images"
 
-    CATEGORY = ""
-
-    def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
+    @classmethod
+    def execute(cls, directory: str, image_load_cap: int = 0, start_index: int = 0):
         if not os.path.isdir(directory):
             raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
         dir_files = os.listdir(directory)
@@ -71,306 +74,283 @@ class LoadImagesFromDirectory:
         if len(images) == 0:
             raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
 
-        return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
+        return io.NodeOutput(torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
 
-
-class ScaledSoftUniversalWeightsDeprecated:
+class ScaledSoftUniversalWeightsDeprecated(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
-                "flip_weights": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ScaledSoftControlNetWeights',
+            display_name='Scaled Soft Weights 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Float.Input('base_multiplier', default=0.825, max=1.0, min=0.0, step=0.001),
+                io.Boolean.Input('flip_weights', default=False),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
-
-    def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+    @classmethod
+    def execute(cls, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class SoftControlNetWeightsDeprecated:
+class SoftControlNetWeightsDeprecated(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "flip_weights": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='SoftControlNetWeights',
+            display_name='ControlNet Soft Weights 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Float.Input('weight_00', default=0.09941396206337118, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_01', default=0.12050177219802567, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_02', default=0.14606275417942507, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_03', default=0.17704576264172736, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_04', default=0.214600924414215, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_05', default=0.26012233262329093, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_06', default=0.3152997971191405, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_07', default=0.3821815722656249, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_08', default=0.4632503906249999, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_09', default=0.561515625, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_10', default=0.6806249999999999, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_11', default=0.825, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('flip_weights', default=False),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
-
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+    @classmethod
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
                      weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                    weight_07, weight_08, weight_09, weight_10, weight_11]
         weights_middle = [weight_12]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class CustomControlNetWeightsDeprecated:
+class CustomControlNetWeightsDeprecated(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "flip_weights": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='CustomControlNetWeights',
+            display_name='ControlNet Custom Weights 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_04', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_05', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_06', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_07', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_08', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_09', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_10', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_11', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('flip_weights', default=False),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
-
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+    @classmethod
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
                      weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                    weight_07, weight_08, weight_09, weight_10, weight_11]
         weights_middle = [weight_12]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class SoftT2IAdapterWeightsDeprecated:
+class SoftT2IAdapterWeightsDeprecated(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "flip_weights": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='SoftT2IAdapterWeights',
+            display_name='T2IAdapter Soft Weights 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Float.Input('weight_00', default=0.25, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_01', default=0.62, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_02', default=0.825, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('flip_weights', default=False),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
-
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
+    @classmethod
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class CustomT2IAdapterWeightsDeprecated:
+class CustomT2IAdapterWeightsDeprecated(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "flip_weights": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='CustomT2IAdapterWeights',
+            display_name='T2IAdapter Custom Weights 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('flip_weights', default=False),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
-
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
+    @classmethod
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class AdvancedControlNetApplyDEPR:
+class AdvancedControlNetApplyDEPR(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "positive": ("CONDITIONING", ),
-                "negative": ("CONDITIONING", ),
-                "control_net": ("CONTROL_NET", ),
-                "image": ("IMAGE", ),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-            },
-            "optional": {
-                "mask_optional": ("MASK", ),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
-                "model_optional": ("MODEL",),
-                "vae_optional": ("VAE",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AdvancedControlNetApply',
+            display_name='Apply Advanced ControlNet 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Conditioning.Input('positive'),
+                io.Conditioning.Input('negative'),
+                io.ControlNet.Input('control_net'),
+                io.Image.Input('image'),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Mask.Input('mask_optional', display_name='effect_mask', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
+                io.Model.Input('model_optional', display_name='model', optional=True),
+                io.Vae.Input('vae_optional', display_name='vae', optional=True)
+            ],
+            outputs=[
+                io.Conditioning.Output('positive', is_output_list=False),
+                io.Conditioning.Output('negative', is_output_list=False),
+                io.Model.Output('model_opt', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",)
-    RETURN_NAMES = ("positive", "negative", "model_opt")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = ""
-
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
+    @classmethod
+    def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent,
                          mask_optional=None, model_optional=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
                          weights_override: ControlWeights=None, control_apply_to_uncond=False):
-        new_positive, new_negative = AdvancedControlNetApply.apply_controlnet(self, positive=positive, negative=negative, control_net=control_net, image=image,
+        new_positive, new_negative = AdvancedControlNetApply.execute(positive=positive, negative=negative, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
-                                                          timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,)
-        return (new_positive, new_negative, model_optional)
+                                                          timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,).args
+        return io.NodeOutput(new_positive, new_negative, model_optional)
 
-
-class AdvancedControlNetApplySingleDEPR:
+class AdvancedControlNetApplySingleDEPR(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "conditioning": ("CONDITIONING", ),
-                "control_net": ("CONTROL_NET", ),
-                "image": ("IMAGE", ),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-            },
-            "optional": {
-                "mask_optional": ("MASK", ),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
-                "model_optional": ("MODEL",),
-                "vae_optional": ("VAE",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AdvancedControlNetApplySingle',
+            display_name='Apply Advanced ControlNet(1) 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Conditioning.Input('conditioning'),
+                io.ControlNet.Input('control_net'),
+                io.Image.Input('image'),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Mask.Input('mask_optional', display_name='effect_mask', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
+                io.Model.Input('model_optional', display_name='model', optional=True),
+                io.Vae.Input('vae_optional', display_name='vae', optional=True)
+            ],
+            outputs=[
+                io.Conditioning.Output('CONDITIONING', is_output_list=False),
+                io.Model.Output('model_opt', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
-    RETURN_TYPES = ("CONDITIONING","MODEL",)
-    RETURN_NAMES = ("CONDITIONING", "model_opt")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = ""
-
-    def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
+    @classmethod
+    def execute(cls, conditioning, control_net, image, strength, start_percent, end_percent,
                          mask_optional=None, model_optional=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
                          weights_override: ControlWeights=None):
-        values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
+        values = AdvancedControlNetApply.execute(positive=conditioning, negative=None, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
                                                           timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
                                                           control_apply_to_uncond=True)
-        return (values[0], model_optional)
+        return io.NodeOutput(values.args[0], model_optional)
 
-
-class ControlNetLoaderAdvancedDEPR:
+class ControlNetLoaderAdvancedDEPR(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
-            },
-            "optional": {
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ControlNetLoaderAdvanced',
+            display_name='Load Advanced ControlNet Model 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', display_name='timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
-
-    CATEGORY = ""
-
-    def load_controlnet(self, control_net_name,
+    @classmethod
+    def execute(cls, control_net_name,
                         tk_optional: TimestepKeyframeGroup=None,
                         timestep_keyframe: TimestepKeyframeGroup=None,
                         ):
@@ -378,32 +358,30 @@ class ControlNetLoaderAdvancedDEPR:
             tk_optional = timestep_keyframe
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
         controlnet = load_controlnet(controlnet_path, tk_optional)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
     
 
-class DiffControlNetLoaderAdvancedDEPR:
+class DiffControlNetLoaderAdvancedDEPR(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), )
-            },
-            "optional": {
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='DiffControlNetLoaderAdvanced',
+            display_name='Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝',
+            category='',
+            inputs=[
+                io.Model.Input('model'),
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', display_name='timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = ""
-
-    def load_controlnet(self, control_net_name, model,
+    @classmethod
+    def execute(cls, control_net_name, model,
                         tk_optional: TimestepKeyframeGroup=None,
                         timestep_keyframe: TimestepKeyframeGroup=None
                         ):
@@ -413,4 +391,4 @@ class DiffControlNetLoaderAdvancedDEPR:
         controlnet = load_controlnet(controlnet_path, tk_optional, model)
         if is_advanced_controlnet(controlnet):
             controlnet.verify_all_weights()
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)

adv_control/nodes_keyframes.py

@@ -1,43 +1,40 @@
+from comfy_api.latest import io
 from typing import Union
 import numpy as np
 from collections.abc import Iterable
 
-from .utils import ControlWeights, TimestepKeyframe, TimestepKeyframeGroup, LatentKeyframe, LatentKeyframeGroup, BIGMIN, BIGMAX
+from .utils import ControlWeights, TimestepKeyframe, TimestepKeyframeGroup, LatentKeyframe, LatentKeyframeGroup
 from .utils import StrengthInterpolation as SI
 from .logger import logger
 
-
-class TimestepKeyframeNode:
+class TimestepKeyframeNode(io.ComfyNode):
     OUTDATED_DUMMY = -39
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "inherit_missing": ("BOOLEAN", {"default": True}, ),
-                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
-                "mask_optional": ("MASK", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='TimestepKeyframe',
+            display_name='Timestep Keyframe 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
+                io.Float.Input('strength', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
+                io.Int.Input('guarantee_steps', optional=True, default=1, max=9007199254740991, min=0),
+                io.Mask.Input('mask_optional', display_name='mask', optional=True)
+            ],
+            outputs=[
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
+    @classmethod
+    def execute(cls,
                       start_percent: float,
                       strength: float=1.0,
                       cn_weights: ControlWeights=None, control_net_weights: ControlWeights=None, # old name
@@ -49,7 +46,7 @@ class TimestepKeyframeNode:
                       guarantee_usage=True, # old input
                       mask_optional=None,):
         # if using outdated dummy value, means node on workflow is outdated and should appropriately convert behavior
-        if guarantee_steps == self.OUTDATED_DUMMY:
+        if guarantee_steps == cls.OUTDATED_DUMMY:
             guarantee_steps = int(guarantee_usage)
         control_net_weights = control_net_weights if control_net_weights else cn_weights
         prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
@@ -61,42 +58,39 @@ class TimestepKeyframeNode:
                                     control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing,
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional)
         prev_timestep_keyframe.add(keyframe)
-        return (prev_timestep_keyframe,)
+        return io.NodeOutput(prev_timestep_keyframe,)
     
 
-class TimestepKeyframeInterpolationNode:
+class TimestepKeyframeInterpolationNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
-                "strength_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
-                "interpolation": (SI._LIST, ),
-                "intervals": ("INT", {"default": 50, "min": 2, "max": 100, "step": 1}),
-            },
-            "optional": {
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},),
-                "inherit_missing": ("BOOLEAN", {"default": True},),
-                "mask_optional": ("MASK", ),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_TimestepKeyframeInterpolation',
+            display_name='Timestep Keyframe Interp. 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('strength_start', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('strength_end', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Combo.Input('interpolation', options=['linear', 'ease-in', 'ease-out', 'ease-in-out']),
+                io.Int.Input('intervals', default=50, max=100, min=2, step=1),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
+                io.Mask.Input('mask_optional', display_name='mask', optional=True),
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
+            ],
+            outputs=[
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
+    @classmethod
+    def execute(cls,
                       start_percent: float, end_percent: float,
                       strength_start: float, strength_end: float, interpolation: str, intervals: int,
                       cn_weights: ControlWeights=None,
@@ -125,39 +119,35 @@ class TimestepKeyframeInterpolationNode:
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional))
             if print_keyframes:
                 logger.info(f"TimestepKeyframe - start_percent:{percent} = {strength}")
-        return (prev_timestep_kf,)
+        return io.NodeOutput(prev_timestep_kf,)
 
-
-class TimestepKeyframeFromStrengthListNode:
+class TimestepKeyframeFromStrengthListNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-            },
-            "optional": {
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},),
-                "inherit_missing": ("BOOLEAN", {"default": True},),
-                "mask_optional": ("MASK", ),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_TimestepKeyframeFromStrengthList',
+            display_name='Timestep Keyframe From List 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Float.Input('float_strengths', default=-1, force_input=True, min=-1, step=0.001),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
+                io.Mask.Input('mask_optional', display_name='mask', optional=True),
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
+            ],
+            outputs=[
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
+    @classmethod
+    def execute(cls,
                       start_percent: float, end_percent: float,
                       float_strengths: float,
                       cn_weights: ControlWeights=None,
@@ -191,32 +181,27 @@ class TimestepKeyframeFromStrengthListNode:
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional))
             if print_keyframes:
                 logger.info(f"TimestepKeyframe - start_percent:{percent} = {strength}")
-        return (prev_timestep_kf,)
+        return io.NodeOutput(prev_timestep_kf,)
 
-
-class LatentKeyframeNode:
+class LatentKeyframeNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "batch_index": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='LatentKeyframe',
+            display_name='Latent Keyframe 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Int.Input('batch_index', default=0, max=9007199254740991, min=-9007199254740991, step=1),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True)
+            ],
+            outputs=[
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
-    FUNCTION = "load_keyframe"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
+    @classmethod
+    def execute(cls,
                       batch_index: int,
                       strength: float,
                       prev_latent_kf: LatentKeyframeGroup=None,
@@ -229,33 +214,29 @@ class LatentKeyframeNode:
             prev_latent_keyframe = prev_latent_keyframe.clone()
         keyframe = LatentKeyframe(batch_index, strength)
         prev_latent_keyframe.add(keyframe)
-        return (prev_latent_keyframe,)
+        return io.NodeOutput(prev_latent_keyframe,)
 
-
-class LatentKeyframeGroupNode:
+class LatentKeyframeGroupNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "index_strengths": ("STRING", {"multiline": True, "default": ""}),
-            },
-            "optional": {
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
-                "latent_optional": ("LATENT", ),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='LatentKeyframeGroup',
+            display_name='Latent Keyframe Group 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.String.Input('index_strengths', default='', multiline=True),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
+                io.Latent.Input('latent_optional', display_name='latent', optional=True),
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
+            ],
+            outputs=[
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
+            ]
+        )
     
-    RETURN_NAMES = ("LATENT_KF", )
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
-    FUNCTION = "load_keyframes"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
+    @staticmethod
+    def validate_index(index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
         # if part of range, do nothing
         if is_range:
             return index
@@ -273,13 +254,15 @@ class LatentKeyframeGroupNode:
             index = conv_index
         return index
 
-    def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
+    @classmethod
+    def convert_to_index_int(cls, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
         try:
-            return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
+            return cls.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
         except ValueError as e:
             raise ValueError(f"index '{raw_index}' must be an integer.", e)
 
-    def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
+    @classmethod
+    def convert_to_latent_keyframes(cls, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
         if not latent_indeces:
             return set()
         int_latent_indeces = [i for i in range(0, latent_count)]
@@ -304,8 +287,8 @@ class LatentKeyframeGroupNode:
             if ':' in g:
                 index_range = g.split(":", 1)
                 index_range = [r.strip() for r in index_range]
-                start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
-                end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                start_index = cls.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                end_index = cls.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
                 # if latents were passed in, base indeces on known latent count
                 if len(int_latent_indeces) > 0:
                     for i in int_latent_indeces[start_index:end_index]:
@@ -316,14 +299,16 @@ class LatentKeyframeGroupNode:
                         chosen_indeces.add(LatentKeyframe(i, strength))
             # parse individual indeces
             else:
-                chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
+                chosen_indeces.add(LatentKeyframe(cls.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
         return chosen_indeces
 
-    def load_keyframes(self,
+    @classmethod
+    def execute(cls,
                        index_strengths: str,
                        prev_latent_kf: LatentKeyframeGroup=None,
                        prev_latent_keyframe: LatentKeyframeGroup=None, # old name
-                       latent_image_opt=None,
+                       latent_optional=None,
+                       latent_image_opt=None, # old name
                        print_keyframes=False):
         prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
         if not prev_latent_keyframe:
@@ -332,10 +317,11 @@ class LatentKeyframeGroupNode:
             prev_latent_keyframe = prev_latent_keyframe.clone()
         curr_latent_keyframe = LatentKeyframeGroup()
 
+        latent_image_opt = latent_image_opt if latent_image_opt is not None else latent_optional
         latent_count = -1
         if latent_image_opt:
             latent_count = latent_image_opt['samples'].size()[0]
-        latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
+        latent_keyframes = cls.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
 
         for latent_keyframe in latent_keyframes:
             curr_latent_keyframe.add(latent_keyframe)
@@ -348,35 +334,32 @@ class LatentKeyframeGroupNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)
 
         
-class LatentKeyframeInterpolationNode:
+class LatentKeyframeInterpolationNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "batch_index_from": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
-                "batch_index_to_excl": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
-                "strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "interpolation": (SI._LIST, ),
-            },
-            "optional": {
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='LatentKeyframeTiming',
+            display_name='Latent Keyframe Interp. 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Int.Input('batch_index_from', default=0, max=9007199254740991, min=-9007199254740991, step=1),
+                io.Int.Input('batch_index_to_excl', default=0, max=9007199254740991, min=-9007199254740991, step=1),
+                io.Float.Input('strength_from', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('strength_to', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Combo.Input('interpolation', options=['linear', 'ease-in', 'ease-out', 'ease-in-out']),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
+            ],
+            outputs=[
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
-    FUNCTION = "load_keyframe"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
+    @classmethod
+    def execute(cls,
                         batch_index_from: int,
                         strength_from: float,
                         batch_index_to_excl: int,
@@ -425,31 +408,27 @@ class LatentKeyframeInterpolationNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)
 
-
-class LatentKeyframeBatchedGroupNode:
+class LatentKeyframeBatchedGroupNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
-            },
-            "optional": {
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='LatentKeyframeBatchedGroup',
+            display_name='Latent Keyframe From List 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
+            inputs=[
+                io.Float.Input('float_strengths', default=-1, force_input=True, min=-1, step=0.001),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
+            ],
+            outputs=[
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
-    FUNCTION = "load_keyframe"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self, float_strengths: Union[float, list[float]],
+    @classmethod
+    def execute(cls, float_strengths: Union[float, list[float]],
                       prev_latent_kf: LatentKeyframeGroup=None,
                       prev_latent_keyframe: LatentKeyframeGroup=None, # old name
                       print_keyframes=False):
@@ -479,4 +458,4 @@ class LatentKeyframeBatchedGroupNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)

adv_control/nodes_main.py

@@ -1,107 +1,130 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 import folder_paths
-from comfy.model_patcher import ModelPatcher
+import comfy.utils
 
 from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
-from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
+from .control_lllite import load_anima_lllite
+from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper
 
-from .logger import logger
-
-
-class ControlNetLoaderAdvanced:
+class ControlNetLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "cnet": (folder_paths.get_filename_list("controlnet"), ),
-            },
-            "optional": {
-                "_tk_opt": ("TIMESTEP_KEYFRAME", ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ControlNetLoaderAdvanced',
+            display_name='Load Advanced ControlNet Model 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝',
+            inputs=[
+                io.Combo.Input('cnet', options=folder_paths.get_filename_list("controlnet")),
+                io.Custom('TIMESTEP_KEYFRAME').Input('_tk_opt', display_name='timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def load_controlnet(self, cnet,
+    @classmethod
+    def execute(cls, cnet,
                         _tk_opt: TimestepKeyframeGroup=None,
                         ):
         controlnet_path = folder_paths.get_full_path("controlnet", cnet)
         controlnet = load_controlnet(controlnet_path, _tk_opt)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
     
 
-class DiffControlNetLoaderAdvanced:
+class DiffControlNetLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "cnet": (folder_paths.get_filename_list("controlnet"), )
-            },
-            "optional": {
-                "_tk_opt": ("TIMESTEP_KEYFRAME", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_DiffControlNetLoaderAdvanced',
+            display_name='Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝',
+            inputs=[
+                io.Model.Input('model'),
+                io.Combo.Input('cnet', options=folder_paths.get_filename_list("controlnet")),
+                io.Custom('TIMESTEP_KEYFRAME').Input('_tk_opt', display_name='timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def load_controlnet(self, cnet, model,
+    @classmethod
+    def execute(cls, cnet, model,
                         _tk_opt: TimestepKeyframeGroup=None,
                         ):
         controlnet_path = folder_paths.get_full_path("controlnet", cnet)
         controlnet = load_controlnet(controlnet_path, _tk_opt, model)
         if is_advanced_controlnet(controlnet):
             controlnet.verify_all_weights()
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
-class AdvancedControlNetApply:
+class AnimaLLLiteLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "positive": ("CONDITIONING", ),
-                "negative": ("CONDITIONING", ),
-                "control_net": ("CONTROL_NET", ),
-                "image": ("IMAGE", ),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-            },
-            "optional": {
-                "mask_optional": ("MASK", ),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
-                "vae_optional": ("VAE",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AnimaLLLiteLoaderAdvanced',
+            display_name='Load Anima LLLite Model 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/loaders',
+            inputs=[
+                io.Combo.Input('model_patch', options=folder_paths.get_filename_list("model_patches")),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
-    RETURN_NAMES = ("positive", "negative")
-    FUNCTION = "apply_controlnet"
+    @classmethod
+    def execute(cls, model_patch, timestep_kf: TimestepKeyframeGroup=None):
+        model_patch_path = folder_paths.get_full_path_or_raise("model_patches", model_patch)
+        return io.NodeOutput(load_anima_lllite(model_patch_path, timestep_keyframe=timestep_kf),)
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
+class AdvancedControlNetApply(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AdvancedControlNetApply_v2',
+            display_name='Apply Advanced ControlNet 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝',
+            inputs=[
+                io.Conditioning.Input('positive'),
+                io.Conditioning.Input('negative'),
+                io.ControlNet.Input('control_net'),
+                io.Image.Input('image'),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Mask.Input('mask_optional', display_name='effect_mask', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
+                io.Vae.Input('vae_optional', display_name='vae', optional=True),
+                io.Mask.Input('inpaint_mask', optional=True)
+            ],
+            outputs=[
+                io.Conditioning.Output('positive', is_output_list=False),
+                io.Conditioning.Output('negative', is_output_list=False)
+            ]
+        )
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
+    @classmethod
+    def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent,
                          mask_optional: Tensor=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
-                         weights_override: ControlWeights=None, control_apply_to_uncond=False):
-        if strength == 0:
-            return (positive, negative)
+                         weights_override: ControlWeights=None, control_apply_to_uncond=False,
+                         inpaint_mask: Tensor=None):
+        if strength == 0 or (mask_optional is not None and mask_optional.count_nonzero().item() == 0):
+            return io.NodeOutput(positive, negative)
+
+        extra_concat = []
+        if inpaint_mask is not None and getattr(control_net, "concat_mask", False):
+            source_mask = 1.0 - inpaint_mask.reshape((-1, 1, inpaint_mask.shape[-2], inpaint_mask.shape[-1]))
+            mask_apply = comfy.utils.common_upscale(source_mask, image.shape[2], image.shape[1], "bilinear", "center").round()
+            image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
+            extra_concat = [source_mask]
 
         control_hint = image.movedim(-1,1)
         cnets = {}
@@ -121,7 +144,7 @@ class AdvancedControlNetApply:
                         if control_net is None:
                             raise Exception("Passed in control_net is None; something must have went wrong when loading it from a Load ControlNet node.")
                         # copy, convert to advanced if needed, and set cond
-                        c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent), vae_optional)
+                        c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent), vae_optional, extra_concat)
                         if is_advanced_controlnet(c_net):
                             # disarm node check
                             c_net.disarm()
@@ -140,9 +163,9 @@ class AdvancedControlNetApply:
                                 elif not vae_optional:
                                     # make sure SD3 ControlNet will get a special message instead of generic type mention
                                     if is_sd3_advanced_controlnet(c_net):
-                                        raise Exception(f"SD3 ControlNet requires vae_optional input, but got None.")
+                                        raise Exception(f"SD3 ControlNet requires vae input, but got None.")
                                     else:
-                                        raise Exception(f"Type '{type(c_net).__name__}' requires vae_optional input, but got None.")
+                                        raise Exception(f"Type '{type(c_net).__name__}' requires vae input, but got None.")
                             # apply optional parameters and overrides, if provided
                             if timestep_kf is not None:
                                 c_net.set_timestep_keyframes(timestep_kf)
@@ -167,46 +190,44 @@ class AdvancedControlNetApply:
                     n = [t[0], d]
                     c.append(n)
             out.append(c)
-        return (out[0], out[1])
+        return io.NodeOutput(out[0], out[1])
     
 
-class AdvancedControlNetApplySingle:
+class AdvancedControlNetApplySingle(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "conditioning": ("CONDITIONING", ),
-                "control_net": ("CONTROL_NET", ),
-                "image": ("IMAGE", ),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-            },
-            "optional": {
-                "mask_optional": ("MASK", ),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
-                "vae_optional": ("VAE",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AdvancedControlNetApplySingle_v2',
+            display_name='Apply Advanced ControlNet(1) 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝',
+            inputs=[
+                io.Conditioning.Input('conditioning'),
+                io.ControlNet.Input('control_net'),
+                io.Image.Input('image'),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Mask.Input('mask_optional', display_name='effect_mask', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
+                io.Vae.Input('vae_optional', display_name='vae', optional=True),
+                io.Mask.Input('inpaint_mask', optional=True)
+            ],
+            outputs=[
+                io.Conditioning.Output('CONDITIONING', is_output_list=False),
+                io.Model.Output('model_opt', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING","MODEL",)
-    RETURN_NAMES = ("CONDITIONING", "model_opt")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
+    @classmethod
+    def execute(cls, conditioning, control_net, image, strength, start_percent, end_percent,
                          mask_optional: Tensor=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
-                         weights_override: ControlWeights=None):
-        values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
+                         weights_override: ControlWeights=None, inpaint_mask: Tensor=None):
+        values = AdvancedControlNetApply.execute(positive=conditioning, negative=None, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
                                                           timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
-                                                          control_apply_to_uncond=True)
-        return (values[0],)
+                                                          control_apply_to_uncond=True, inpaint_mask=inpaint_mask)
+        return io.NodeOutput(values.args[0], None)

adv_control/nodes_plusplus.py

@@ -1,81 +1,82 @@
+from comfy_api.latest import io
 from torch import Tensor
 import math
 
 import folder_paths
 
-from .control_plusplus import load_controlnetplusplus, PlusPlusType, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
-from .utils import BIGMAX
+from .control_plusplus import load_controlnetplusplus, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
 
-
-class PlusPlusLoaderAdvanced:
+class PlusPlusLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "plus_input": ("PLUS_INPUT", ),
-                "name": (folder_paths.get_filename_list("controlnet"), ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ControlNet++LoaderAdvanced',
+            display_name='Load ControlNet++ Model (Multi) 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
+            inputs=[
+                io.Custom('PLUS_INPUT').Input('plus_input'),
+                io.Combo.Input('name', options=folder_paths.get_filename_list("controlnet"))
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False),
+                io.Image.Output('IMAGE', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", "IMAGE",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
-
-    def load_controlnet_plusplus(self, plus_input: PlusPlusInputGroup, name: str):
+    @classmethod
+    def execute(cls, plus_input: PlusPlusInputGroup, name: str):
         controlnet_path = folder_paths.get_full_path("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.verify_control_type(name, plus_input)
         controlnet.allow_condhint_latents = True
-        return (controlnet, PlusPlusImageWrapper(plus_input),)
+        return io.NodeOutput(controlnet, PlusPlusImageWrapper(plus_input),)
 
-
-class PlusPlusLoaderSingle:
+class PlusPlusLoaderSingle(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "name": (folder_paths.get_filename_list("controlnet"), ),
-                "control_type": (PlusPlusType._LIST_WITH_NONE, {"default": PlusPlusType.NONE}, ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ControlNet++LoaderSingle',
+            display_name='Load ControlNet++ Model (Single) 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
+            inputs=[
+                io.Combo.Input('name', options=folder_paths.get_filename_list("controlnet")),
+                io.Combo.Input('control_type', options=['openpose', 'depth', 'hed/pidi/scribble/ted', 'canny/lineart/mlsd', 'normal', 'segment', 'tile', 'inpaint/outpaint', 'none'], default='none')
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
-
-    def load_controlnet_plusplus(self, name: str, control_type: str):
+    @classmethod
+    def execute(cls, name: str, control_type: str):
         controlnet_path = folder_paths.get_full_path("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.single_control_type = control_type
         controlnet.verify_control_type(name)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
-class PlusPlusInputNode:
+class PlusPlusInputNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "control_type": (PlusPlusType._LIST,),
-            },
-            "optional": {
-                "prev_plus_input": ("PLUS_INPUT",),
-                #"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": BIGMAX, "step": 0.01}),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ControlNet++InputNode',
+            display_name='ControlNet++ Input 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
+            inputs=[
+                io.Image.Input('image'),
+                io.Combo.Input('control_type', options=['openpose', 'depth', 'hed/pidi/scribble/ted', 'canny/lineart/mlsd', 'normal', 'segment', 'tile', 'inpaint/outpaint']),
+                io.Custom('PLUS_INPUT').Input('prev_plus_input', optional=True)
+            ],
+            outputs=[
+                io.Custom('PLUS_INPUT').Output('PLUS_INPUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("PLUS_INPUT", )
-    FUNCTION = "wrap_images"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
-
-    def wrap_images(self, image: Tensor, control_type: str, strength=1.0, prev_plus_input: PlusPlusInputGroup=None):
+    @classmethod
+    def execute(cls, image: Tensor, control_type: str, strength=1.0, prev_plus_input: PlusPlusInputGroup=None):
         if prev_plus_input is None:
             prev_plus_input = PlusPlusInputGroup()
         prev_plus_input = prev_plus_input.clone()
@@ -85,4 +86,4 @@ class PlusPlusInputNode:
         pp_input = PlusPlusInput(image, control_type, strength)
         prev_plus_input.add(pp_input)
 
-        return (prev_plus_input,)
+        return io.NodeOutput(prev_plus_input,)

adv_control/nodes_reference.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 from nodes import VAEEncode
@@ -6,77 +7,81 @@ from comfy.sd import VAE
 
 from .control_reference import ReferenceAdvanced, ReferenceOptions, ReferenceType, ReferencePreprocWrapper
 
-
 # node for ReferenceCN
-class ReferenceControlNetNode:
+class ReferenceControlNetNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "reference_type": (ReferenceType._LIST,),
-                "style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-            },
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ReferenceControlNet',
+            display_name='Reference ControlNet 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference',
+            inputs=[
+                io.Combo.Input('reference_type', options=['reference_attn', 'reference_adain', 'reference_attn+adain']),
+                io.Float.Input('style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('ref_weight', default=1.0, max=1.0, min=0.0, step=0.01)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference"
-
-    def load_controlnet(self, reference_type: str, style_fidelity: float, ref_weight: float):
+    @classmethod
+    def execute(cls, reference_type: str, style_fidelity: float, ref_weight: float):
         ref_opts = ReferenceOptions.create_combo(reference_type=reference_type, style_fidelity=style_fidelity, ref_weight=ref_weight)
         controlnet = ReferenceAdvanced(ref_opts=ref_opts, timestep_keyframes=None)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
-class ReferenceControlFinetune:
+class ReferenceControlFinetune(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "attn_style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "attn_ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "adain_style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "adain_ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "adain_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-            },
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ReferenceControlNetFinetune',
+            display_name='Reference ControlNet (Finetune) 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference',
+            inputs=[
+                io.Float.Input('attn_style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('attn_ref_weight', default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('attn_strength', default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('adain_style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('adain_ref_weight', default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('adain_strength', default=1.0, max=1.0, min=0.0, step=0.01)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference"
-
-    def load_controlnet(self,
+    @classmethod
+    def execute(cls,
                         attn_style_fidelity: float, attn_ref_weight: float, attn_strength: float,
                         adain_style_fidelity: float, adain_ref_weight: float, adain_strength: float):
         ref_opts = ReferenceOptions(reference_type=ReferenceType.ATTN_ADAIN,
                                     attn_style_fidelity=attn_style_fidelity, attn_ref_weight=attn_ref_weight, attn_strength=attn_strength,
                                     adain_style_fidelity=adain_style_fidelity, adain_ref_weight=adain_ref_weight, adain_strength=adain_strength)
         controlnet = ReferenceAdvanced(ref_opts=ref_opts, timestep_keyframes=None)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
-class ReferencePreprocessorNode:
+class ReferencePreprocessorNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "vae": ("VAE", ),
-                "latent_size": ("LATENT", ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ReferencePreprocessor',
+            display_name='Reference Preproccessor 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference/preprocess',
+            inputs=[
+                io.Image.Input('image'),
+                io.Vae.Input('vae'),
+                io.Latent.Input('latent_size')
+            ],
+            outputs=[
+                io.Image.Output('proc_IMAGE', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("proc_IMAGE",)
-    FUNCTION = "preprocess_images"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference/preprocess"
-
-    def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
+    @classmethod
+    def execute(cls, vae: VAE, image: Tensor, latent_size: Tensor):
         # first, resize image to match latents
         image = image.movedim(-1,1)
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
@@ -87,4 +92,4 @@ class ReferencePreprocessorNode:
         except Exception:
             image = VAEEncode.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
-        return (ReferencePreprocWrapper(condhint=encoded),)
+        return io.NodeOutput(ReferencePreprocWrapper(condhint=encoded),)

adv_control/nodes_sparsectrl.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 import folder_paths
@@ -7,68 +8,68 @@ from comfy.sd import VAE
 
 from .utils import TimestepKeyframeGroup
 from .control_sparsectrl import SparseMethod, SparseIndexMethod, SparseSettings, SparseSpreadMethod, PreprocSparseRGBWrapper, SparseConst, SparseContextAware, get_idx_list_from_str
-from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced, SparseCtrlAdvanced
-
+from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced
 
 # node for SparseCtrl loading
-class SparseCtrlLoaderAdvanced:
+class SparseCtrlLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
-                "use_motion": ("BOOLEAN", {"default": True}, ),
-                "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "sparse_method": ("SPARSE_METHOD", ),
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
-                "context_aware": (SparseContextAware.LIST, ),
-                "sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlLoaderAdvanced',
+            display_name='Load SparseCtrl Model 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
+            inputs=[
+                io.Combo.Input('sparsectrl_name', options=folder_paths.get_filename_list("controlnet")),
+                io.Boolean.Input('use_motion', default=True),
+                io.Float.Input('motion_strength', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('motion_scale', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Custom('SPARSE_METHOD').Input('sparse_method', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', display_name='timestep_kf', optional=True),
+                io.Combo.Input('context_aware', optional=True, options=['nearest_hint', 'off']),
+                io.Float.Input('sparse_hint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('sparse_nonhint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('sparse_mask_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
-
-    def load_controlnet(self, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
+    @classmethod
+    def execute(cls, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
                         context_aware=SparseContextAware.NEAREST_HINT, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
         sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale,
                                          context_aware=context_aware,
                                          sparse_mask_mult=sparse_mask_mult, sparse_hint_mult=sparse_hint_mult, sparse_nonhint_mult=sparse_nonhint_mult)
         sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
-        return (sparsectrl,)
+        return io.NodeOutput(sparsectrl,)
 
-
-class SparseCtrlMergedLoaderAdvanced:
+class SparseCtrlMergedLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
-                "use_motion": ("BOOLEAN", {"default": True}, ),
-                "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "sparse_method": ("SPARSE_METHOD", ),
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlMergedLoaderAdvanced',
+            display_name='🧪Load Merged SparseCtrl Model 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental',
+            inputs=[
+                io.Combo.Input('sparsectrl_name', options=folder_paths.get_filename_list("controlnet")),
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
+                io.Boolean.Input('use_motion', default=True),
+                io.Float.Input('motion_strength', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('motion_scale', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Custom('SPARSE_METHOD').Input('sparse_method', optional=True),
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', display_name='timestep_kf', optional=True)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental"
-
-    def load_controlnet(self, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
+    @classmethod
+    def execute(cls, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
         sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale, merged=True)
@@ -85,67 +86,68 @@ class SparseCtrlMergedLoaderAdvanced:
             new_state_dict[key] = value
         # now, reload sparsectrl with real settings
         sparsectrl = load_sparsectrl(sparsectrl_path, controlnet_data=new_state_dict, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
-        return (sparsectrl,)
+        return io.NodeOutput(sparsectrl,)
 
-
-class SparseIndexMethodNode:
+class SparseIndexMethodNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "indexes": ("STRING", {"default": "0"}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlIndexMethodNode',
+            display_name='SparseCtrl Index Method 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
+            inputs=[
+                io.String.Input('indexes', default='0')
+            ],
+            outputs=[
+                io.Custom('SPARSE_METHOD').Output('SPARSE_METHOD', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("SPARSE_METHOD",)
-    FUNCTION = "get_method"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
-
-    def get_method(self, indexes: str):
+    @classmethod
+    def execute(cls, indexes: str):
         idxs = get_idx_list_from_str(indexes)
-        return (SparseIndexMethod(idxs),)
+        return io.NodeOutput(SparseIndexMethod(idxs),)
 
-
-class SparseSpreadMethodNode:
+class SparseSpreadMethodNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "spread": (SparseSpreadMethod.LIST,),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlSpreadMethodNode',
+            display_name='SparseCtrl Spread Method 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
+            inputs=[
+                io.Combo.Input('spread', options=['uniform', 'starting', 'ending', 'center'])
+            ],
+            outputs=[
+                io.Custom('SPARSE_METHOD').Output('SPARSE_METHOD', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("SPARSE_METHOD",)
-    FUNCTION = "get_method"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
-
-    def get_method(self, spread: str):
-        return (SparseSpreadMethod(spread=spread),)
-
-
-class RgbSparseCtrlPreprocessor:
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "vae": ("VAE", ),
-                "latent_size": ("LATENT", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def execute(cls, spread: str):
+        return io.NodeOutput(SparseSpreadMethod(spread=spread),)
 
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("proc_IMAGE",)
-    FUNCTION = "preprocess_images"
+class RgbSparseCtrlPreprocessor(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlRGBPreprocessor',
+            display_name='RGB SparseCtrl 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess',
+            inputs=[
+                io.Image.Input('image'),
+                io.Vae.Input('vae'),
+                io.Latent.Input('latent_size')
+            ],
+            outputs=[
+                io.Image.Output('proc_IMAGE', is_output_list=False)
+            ]
+        )
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess"
-
-    def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
+    @classmethod
+    def execute(cls, vae: VAE, image: Tensor, latent_size: Tensor):
         # first, resize image to match latents
         image = image.movedim(-1,1)
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
@@ -156,33 +158,31 @@ class RgbSparseCtrlPreprocessor:
         except Exception:
             image = VAEEncode.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
-        return (PreprocSparseRGBWrapper(condhint=encoded),)
+        return io.NodeOutput(PreprocSparseRGBWrapper(condhint=encoded),)
 
-
-class SparseWeightExtras:
+class SparseWeightExtras(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "optional": {
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-                "sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlWeightExtras',
+            display_name='SparseCtrl Weight Extras 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras',
+            inputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True),
+                io.Float.Input('sparse_hint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('sparse_nonhint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('sparse_mask_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001)
+            ],
+            outputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )
-    RETURN_NAMES = ("cn_extras", )
-    FUNCTION = "create_weight_extras"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras"
-
-    def create_weight_extras(self, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
+    @classmethod
+    def execute(cls, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
         cn_extras = cn_extras.copy()
         cn_extras[SparseConst.HINT_MULT] = sparse_hint_mult
         cn_extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult
         cn_extras[SparseConst.MASK_MULT] = sparse_mask_mult
-        return (cn_extras, )
+        return io.NodeOutput(cn_extras, )

adv_control/nodes_weight.py

@@ -1,62 +1,56 @@
+from comfy_api.latest import io
 from torch import Tensor
 import torch
 from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, Extras, get_properly_arranged_t2i_weights, linear_conversion
-from .logger import logger
-
+from .control_lllite import AnimaLLLiteConst
 
 WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
 
-
-class DefaultWeights:
+class DefaultWeights(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "optional": {
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_DefaultUniversalWeights',
+            display_name='Default Weights 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
+            inputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
-
-    def load_weights(self, cn_extras: dict[str]={}):
+    @classmethod
+    def execute(cls, cn_extras: dict[str]={}):
         weights = ControlWeights.default(extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class ScaledSoftMaskedUniversalWeights:
+class ScaledSoftMaskedUniversalWeights(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "mask": ("MASK", ),
-                "min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
-                "max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
-                #"lock_min": ("BOOLEAN", {"default": False}, ),
-                #"lock_max": ("BOOLEAN", {"default": False}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ScaledSoftMaskedUniversalWeights',
+            display_name='Scaled Soft Masked Weights 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
+            inputs=[
+                io.Mask.Input('mask'),
+                io.Float.Input('min_base_multiplier', default=0.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('max_base_multiplier', default=1.0, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
-
-    def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
+    @classmethod
+    def execute(cls, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         # normalize mask
         mask = mask.clone()
@@ -67,116 +61,107 @@ class ScaledSoftMaskedUniversalWeights:
         else:
             mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
         weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class ScaledSoftUniversalWeights:
+class ScaledSoftUniversalWeights(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ScaledSoftControlNetWeights',
+            display_name='Scaled Soft Weights 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
+            inputs=[
+                io.Float.Input('base_multiplier', default=0.825, max=1.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
-
-    def load_weights(self, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+    @classmethod
+    def execute(cls, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class SoftControlNetWeightsSD15:
+class SoftControlNetWeightsSD15(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "output_0": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_1": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_2": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_3": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_4": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_5": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_6": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_7": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_8": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_9": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SoftControlNetWeightsSD15',
+            display_name='ControlNet Soft Weights [SD1.5] 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
+            inputs=[
+                io.Float.Input('output_0', default=0.09941396206337118, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_1', default=0.12050177219802567, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_2', default=0.14606275417942507, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_3', default=0.17704576264172736, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_4', default=0.214600924414215, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_5', default=0.26012233262329093, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_6', default=0.3152997971191405, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_7', default=0.3821815722656249, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_8', default=0.4632503906249999, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_9', default=0.561515625, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_10', default=0.6806249999999999, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_11', default=0.825, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('middle_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
-
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
+    @classmethod
+    def execute(cls, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                      output_7, output_8, output_9, output_10, output_11, middle_0,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-        return CustomControlNetWeightsSD15.load_weights(self,
+        return CustomControlNetWeightsSD15.execute(
                                                         output_0=output_0, output_1=output_1, output_2=output_2, output_3=output_3,
                                                         output_4=output_4, output_5=output_5, output_6=output_6, output_7=output_7,
                                                         output_8=output_8, output_9=output_9, output_10=output_10, output_11=output_11,
                                                         middle_0=middle_0,
                                                         uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)
 
-
-class CustomControlNetWeightsSD15:
+class CustomControlNetWeightsSD15(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "output_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "output_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_CustomControlNetWeightsSD15',
+            display_name='ControlNet Custom Weights [SD1.5] 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
+            inputs=[
+                io.Float.Input('output_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_1', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_2', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_3', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_4', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_5', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_6', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_7', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_8', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_9', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_10', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('output_11', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('middle_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
-
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
+    @classmethod
+    def execute(cls, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                      output_7, output_8, output_9, output_10, output_11, middle_0,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
@@ -184,50 +169,47 @@ class CustomControlNetWeightsSD15:
         weights_middle = [middle_0]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier,
                                             extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class CustomControlNetWeightsFlux:
+class CustomControlNetWeightsFlux(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_13": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_14": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_15": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_16": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_17": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_18": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_CustomControlNetWeightsFlux',
+            display_name='ControlNet Custom Weights [Flux] 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
+            inputs=[
+                io.Float.Input('input_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_1', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_2', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_4', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_5', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_6', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_7', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_8', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_9', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_10', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_11', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_12', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_13', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_14', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_15', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_16', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_17', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_18', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
-
-    def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6, 
+    @classmethod
+    def execute(cls, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
                      input_7, input_8, input_9, input_10, input_11, input_12, input_13,
                      input_14, input_15, input_16, input_17, input_18,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
@@ -235,95 +217,162 @@ class CustomControlNetWeightsFlux:
                          input_6, input_7, input_8, input_9, input_10, input_11,
                          input_12, input_13, input_14, input_15, input_16, input_17, input_18]
         weights = ControlWeights.controlnet(weights_input=weights_input, uncond_multiplier=uncond_multiplier, extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class SoftT2IAdapterWeights:
+class CustomControlNetWeightsAnima(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_0": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_1": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_2": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_CustomControlNetWeightsAnima',
+            display_name='ControlNet Custom Weights [Anima] 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
+            inputs=[
+                io.Float.Input('block_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_1', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_2', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_3', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_4', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_5', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_6', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_7', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_8', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_9', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_10', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_11', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_12', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_13', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_14', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_15', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_16', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_17', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_18', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_19', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_20', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_21', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_22', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_23', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_24', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_25', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_26', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_27', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
+
+    @classmethod
+    def execute(cls, uncond_multiplier: float=1.0, cn_extras: dict[str]={}, **kwargs):
+        weights = [kwargs[f"block_{index}"] for index in range(28)]
+        control_weights = ControlWeights.controllllite(
+            weights_input=weights,
+            uncond_multiplier=uncond_multiplier,
+            extras=cn_extras,
+        )
+        return io.NodeOutput(control_weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=control_weights)))
+
+class SoftT2IAdapterWeights(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SoftT2IAdapterWeights',
+            display_name='T2IAdapter Soft Weights 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter',
+            inputs=[
+                io.Float.Input('input_0', default=0.25, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_1', default=0.62, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_2', default=0.825, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
-
-    def load_weights(self, input_0, input_1, input_2, input_3,
+    @classmethod
+    def execute(cls, input_0, input_1, input_2, input_3,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-        return CustomT2IAdapterWeights.load_weights(self, input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
+        return CustomT2IAdapterWeights.execute(input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
                                                     uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)
 
-
-class CustomT2IAdapterWeights:
+class CustomT2IAdapterWeights(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
-            },
-            "optional": {
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_CustomT2IAdapterWeights',
+            display_name='T2IAdapter Custom Weights 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter',
+            inputs=[
+                io.Float.Input('input_0', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_1', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_2', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
-
-    def load_weights(self, input_0, input_1, input_2, input_3,
+    @classmethod
+    def execute(cls, input_0, input_1, input_2, input_3,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [input_0, input_1, input_2, input_3]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
-class ExtrasMiddleMultNode:
+class ExtrasMiddleMultNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "middle_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
-            },
-            "optional": {
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_ExtrasMiddleMult',
+            display_name='Middle Weight Extras 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/extras',
+            inputs=[
+                io.Float.Input('middle_mult', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS",)
-    RETURN_NAMES = ("cn_extras",)
-    FUNCTION = "create_extras"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/extras"
-
-    def create_extras(self, middle_mult: float, cn_extras: dict[str]={}):
+    @classmethod
+    def execute(cls, middle_mult: float, cn_extras: dict[str]={}):
         cn_extras = cn_extras.copy()
         cn_extras[Extras.MIDDLE_MULT] = middle_mult
-        return (cn_extras,)
+        return io.NodeOutput(cn_extras,)
+
+class AnimaLLLiteExtras(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_AnimaLLLiteExtras',
+            display_name='Anima LLLite Extras 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/extras',
+            inputs=[
+                io.Mask.Input('inpaint_mask'),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
+            ]
+        )
+
+    @classmethod
+    def execute(cls, inpaint_mask: Tensor, cn_extras: dict[str]={}):
+        cn_extras = cn_extras.copy()
+        cn_extras[AnimaLLLiteConst.INPAINT_MASK] = inpaint_mask.clone()
+        return io.NodeOutput(cn_extras,)

adv_control/utils.py

@@ -359,7 +359,7 @@ def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim
     mask = mask.clone()
     if flux_shape is not None:
         multiplier = multiplier * 0.5
-        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(round(flux_shape[-2]*multiplier), round(flux_shape[-1]*multiplier)), mode="bilinear")
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(math.ceil(flux_shape[-2]*multiplier), math.ceil(flux_shape[-1]*multiplier)), mode="bilinear")
         mask = rearrange(mask, "b c h w -> b (h w) c")
     else:
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(round(shape[-2]*multiplier), round(shape[-1]*multiplier)), mode="bilinear")

examples/anima_lllite_v2/README.md

@@ -0,0 +1,144 @@
+# Anima LLLite validation workflows
+
+This folder contains simple Advanced-ControlNet workflows for the two Anima
+LLLite v2 checkpoints: the five conditioning inputs documented for the v2
+any-test-like model and the v2 inpainting model. It also includes vanilla parity
+and effect-mask validation workflows.
+
+![Simple workflows for all six v2 examples](https://ampcode.com/attachments/3ef84a8ad6947c624cd82b5ac6fb9c664b224678c774a5eba79f4f7e6e8d5c3f.jpeg)
+
+![Control images and tested results for all six v2 examples](https://ampcode.com/attachments/addd1fdb85aa331cc622ef55a9397a996f34c5243e268a1b9bcc42ca39a25120.jpeg)
+
+## Simple v2 workflows
+
+| Type | Workflow | Input image | Checkpoint |
+| --- | --- | --- | --- |
+| Any - Grayscale A | [`anima_lllite_any_grayscale_a.json`](anima_lllite_any_grayscale_a.json) | [`anima_lllite_any_grayscale_a_control.png`](https://ampcode.com/attachments/911b31c414bcf5c9aeaa254356c89f04e1133ce2181d0414cd8b7e9a7fa7847c.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - Grayscale B | [`anima_lllite_any_grayscale_b.json`](anima_lllite_any_grayscale_b.json) | [`anima_lllite_any_grayscale_b_control.png`](https://ampcode.com/attachments/6950b3c8a47ff62311cc8ac147e2c8aa6aad9176abe7046db3a1bd6cf97cf705.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - Lineart | [`anima_lllite_any_lineart.json`](anima_lllite_any_lineart.json) | [`anima_lllite_any_lineart_control.png`](https://ampcode.com/attachments/338c2f07267d564ca9fb60bf79c229b5689509202734875f0cb674efb8d691b1.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - HED scribble | [`anima_lllite_any_hed_scribble.json`](anima_lllite_any_hed_scribble.json) | [`anima_lllite_any_hed_scribble_control.png`](https://ampcode.com/attachments/a52217f09c5852652667152cb901b98c3472c2e4cbf86a6cae53a36ec7e84192.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Any - PiDiNet scribble | [`anima_lllite_any_pidinet_scribble.json`](anima_lllite_any_pidinet_scribble.json) | [`anima_lllite_any_pidinet_scribble_control.png`](https://ampcode.com/attachments/57bc6b0d8cba0df17f92115917bfc6ccfce83107e56af624f0be1db32c402579.png) | `anima-lllite-any-test-like-v2.safetensors` |
+| Inpainting | [`anima_lllite_inpainting.json`](anima_lllite_inpainting.json) | [`anima_lllite_v2_control.png`](https://ampcode.com/attachments/3220b2f533a014619e3c0422eca8a3343e8488eb113795af5bae5f05d38a8ada.png) | `anima-lllite-inpainting-v2.safetensors` |
+
+Download the selected input image from the table and save it under the displayed
+filename in `ComfyUI/input`. Load its workflow and queue it unchanged. Results
+are saved under `ComfyUI/output/acn_anima_examples`. Binary inputs and
+screenshots are linked externally instead of being committed to this repository.
+
+These examples use default node names and do not connect the custom 28-layer
+Anima weights node. The inpainting workflow uses Anima LLLite Extras and
+Default Weights only because the model's source mask must be carried through
+`cn_extras`. Strength, start/end scheduling, effect masks, timestep keyframes,
+latent keyframes, and stacking remain available on the standard
+Advanced-ControlNet nodes.
+
+The model author trained the v2 any-test-like checkpoint on five conditioning
+types: HED scribble, PiDiNet scribble, Grayscale A, Grayscale B, and lineart,
+all with heavy augmentation. The examples above exercise each input type using
+that one v2 checkpoint instead of the lower-quality Preview3 depth, pose,
+lineart, and scribble checkpoints.
+
+The HED and PiDiNet controls were prepared with the
+[comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux)
+HED and Scribble PiDiNet preprocessors. The HED soft-edge output was thresholded
+at 80 to make the documented white-on-black scribble representation. The model
+card names two grayscale generation patterns but does not define their
+individual construction; Grayscale A is the author's sample and Grayscale B
+demonstrates the documented inversion, contrast, and blur augmentation.
+
+## Requirements
+
+Official Hugging Face repositories:
+
+- [circlestone-labs/Anima](https://huggingface.co/circlestone-labs/Anima) - base model, text encoder, and VAE
+- [kohya-ss/Anima-LLLite](https://huggingface.co/kohya-ss/Anima-LLLite) - Anima LLLite control models
+
+Use ComfyUI commit `0f42ba514631` or later and place these files in the listed
+model folders. These are direct downloads from the official model repositories:
+
+| Download | ComfyUI model folder |
+| --- | --- |
+| [`anima-base-v1.0.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors?download=true) | `models/diffusion_models` |
+| [`qwen_3_06b_base.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors?download=true) | `models/text_encoders` |
+| [`qwen_image_vae.safetensors`](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/vae/qwen_image_vae.safetensors?download=true) | `models/vae` |
+| [`anima-lllite-inpainting-v2.safetensors`](https://huggingface.co/kohya-ss/Anima-LLLite/resolve/main/anima-lllite-inpainting-v2.safetensors?download=true) | `models/model_patches` |
+| [`anima-lllite-any-test-like-v2.safetensors`](https://huggingface.co/kohya-ss/Anima-LLLite/resolve/main/anima-lllite-any-test-like-v2.safetensors?download=true) (3-channel any-type model) | `models/model_patches` |
+
+The included workflows use **Load Anima LLLite Model**, so their LLLite files
+belong in `models/model_patches`, matching vanilla ComfyUI. For compatibility
+with existing Advanced-ControlNet LLLite workflows, the files may instead be
+placed in `models/controlnet` and loaded with **Load Advanced ControlNet
+Model**. The standard loader automatically distinguishes Anima checkpoints
+from older SDXL LLLite checkpoints.
+
+## Run the inpainting comparison
+
+This validation workflow runs the inpainting model through vanilla ComfyUI and
+Advanced-ControlNet with identical inputs and sampling settings. It saves both
+decoded results, both latent tensors, and an absolute pixel-difference image.
+
+![Workflow showing the vanilla and Advanced-ControlNet branches](https://ampcode.com/attachments/f056cae89132c45bc133f456a2832a81255fee9c0782968a24adce2095b2fe1b.jpeg)
+
+![Bit-exact result comparison](https://ampcode.com/attachments/fcf073d1253ea721a2d46d34efde0e45ff6ccfb751fbd295a438ab459cc26037.jpeg)
+
+1. Download [`anima_lllite_v2_control.png`](https://ampcode.com/attachments/3220b2f533a014619e3c0422eca8a3343e8488eb113795af5bae5f05d38a8ada.png)
+   to `ComfyUI/input`.
+2. Load `anima_lllite_v2_inpaint_comparison.json` in ComfyUI.
+3. Queue the workflow without changing its settings.
+4. Inspect `ComfyUI/output/acn_anima_pr`.
+
+The control PNG contains a transparent edit region. ComfyUI's Load Image node
+provides that alpha channel as the source inpainting mask.
+
+The vanilla branch passes the mask directly to Apply Anima LLLite. The
+Advanced-ControlNet branch passes it through Anima LLLite Extras, the
+`cn_extras` input on Default Weights, and `weights_override` on Apply Advanced
+ControlNet.
+
+If an inpainting checkpoint reaches sampling without that source mask,
+Advanced-ControlNet raises an error that describes these connections instead
+of silently substituting an empty mask.
+
+With the included seed and settings, the expected results are:
+
+- Identical latent tensors with maximum and mean absolute differences of `0.0`.
+- Identical decoded PNG pixels.
+- A completely black `absolute_difference` image.
+
+## Run the any-type effect-mask comparison
+
+This workflow verifies the official 3-channel any-type checkpoint against
+vanilla ComfyUI and applies an Advanced-ControlNet effect mask to only the left
+half of the image. Its nodes retain their default names; the colored regions
+identify the comparison branches.
+
+![Any-type effect-mask workflow](https://ampcode.com/attachments/3a547b4914a84f0d578b0223149d3ed14e9b22542093e4bc51b6501aeb18f29f.jpeg)
+
+![Any-type parity and effect-mask results](https://ampcode.com/attachments/340c286b28d74d943f03a95d71208f2408c94934f0809fbb060cc774ae8c1b67.jpeg)
+
+1. Download [`anima_lllite_v2_any_control.png`](https://ampcode.com/attachments/db18d41c476324bdf5a5b6117476ed117cc590f8f48e193f6316d08b959bc49c.png)
+   and [`anima_lllite_v2_left_half_mask.png`](https://ampcode.com/attachments/e4fe3a88415357f29315c9afb124ee975a48dee8abccad1fe1903dd5d21b91b6.png)
+   to `ComfyUI/input`.
+2. Load `anima_lllite_v2_any_effect_mask.json` in ComfyUI.
+3. Queue the workflow without changing its settings.
+4. Inspect `ComfyUI/output/acn_anima_any_mask`.
+
+The expected results are:
+
+- Advanced-ControlNet full control and vanilla full control have identical
+  latent tensors and decoded pixels, with maximum absolute difference `0.0`.
+- A completely black effect mask produces the no-control latent exactly.
+- A completely white effect mask produces the unmasked full-control latent
+  exactly.
+- At the model token resolution, the included half mask is exactly `1.0` on
+  the left and `0.0` on the right. Direct LLLite injection is therefore zero
+  for every right-side token.
+- In the final image, the masked right side is closer to the no-control
+  baseline than full control: PSNR improves from `16.39` to `17.69`, and SSIM
+  improves from `0.554` to `0.600`.
+
+The final right half is not pixel-identical to the no-control image. This model
+patches self-attention Q, so controlled left-side tokens can influence
+right-side tokens through global self-attention and later diffusion steps.
+The effect mask guarantees local control injection, not hard image-space
+isolation after attention.

examples/anima_lllite_v2/anima_lllite_any_grayscale_a.json

@@ -0,0 +1,941 @@
+{
+  "id": "00000000-0000-0000-0000-000000000000",
+  "revision": 0,
+  "last_node_id": 12,
+  "last_link_id": 26,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "UNETLoader",
+      "pos": [
+        50,
+        100
+      ],
+      "size": [
+        270,
+        82
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "unet_name",
+          "name": "unet_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "unet_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "weight_dtype",
+          "name": "weight_dtype",
+          "type": "COMBO",
+          "widget": {
+            "name": "weight_dtype"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "MODEL",
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            20
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "UNETLoader",
+        "models": [
+          {
+            "name": "anima-base-v1.0.safetensors",
+            "url": "https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors",
+            "directory": "diffusion_models"
+          }
+        ]
+      },
+      "widgets_values": [
+        "anima-base-v1.0.safetensors",
+        "default"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "CLIPLoader",
+      "pos": [
+        50,
+        250
+      ],
+      "size": [
+        270,
+        106
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip_name",
+          "name": "clip_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "clip_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "type",
+          "name": "type",
+          "type": "COMBO",
+          "widget": {
+            "name": "type"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "device",
+          "name": "device",
+          "shape": 7,
+          "type": "COMBO",
+          "widget": {
+            "name": "device"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            14,
+            15
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPLoader",
+        "models": [
+          {
+            "name": "qwen_3_06b_base.safetensors",
+            "url": "https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors",
+            "directory": "text_encoders"
+          }
+        ]
+      },
+      "widgets_values": [
+        "qwen_3_06b_base.safetensors",
+        "qwen_image",
+        "default"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "CLIPTextEncode",
+      "pos": [
+        360,
+        190
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 14
+        },
+        {
+          "localized_name": "text",
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            16
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "anime illustration of a female knight in ornate silver plate armor holding a sword, side profile in a dense forest, detailed"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CLIPTextEncode",
+      "pos": [
+        360,
+        440
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 15
+        },
+        {
+          "localized_name": "text",
+          "name": "text",
+          "type": "STRING",
+          "widget": {
+            "name": "text"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            17
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptySD3LatentImage",
+      "pos": [
+        50,
+        470
+      ],
+      "size": [
+        270,
+        106
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "batch_size",
+          "name": "batch_size",
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examples/anima_lllite_v2/anima_lllite_any_pidinet_scribble.json

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+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "timestep_kf",
+          "name": "timestep_kf",
+          "shape": 7,
+          "type": "TIMESTEP_KEYFRAME",
+          "link": null
+        },
+        {
+          "localized_name": "model_patch",
+          "name": "model_patch",
+          "type": "COMBO",
+          "widget": {
+            "name": "model_patch"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "CONTROL_NET",
+          "name": "CONTROL_NET",
+          "type": "CONTROL_NET",
+          "links": [
+            18
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ACN_AnimaLLLiteLoaderAdvanced",
+        "models": [
+          {
+            "name": "anima-lllite-any-test-like-v2.safetensors",
+            "url": "https://huggingface.co/kohya-ss/Anima-LLLite/resolve/main/anima-lllite-any-test-like-v2.safetensors",
+            "directory": "model_patches"
+          }
+        ]
+      },
+      "widgets_values": [
+        "anima-lllite-any-test-like-v2.safetensors"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "ACN_AdvancedControlNetApply_v2",
+      "pos": [
+        1210,
+        130
+      ],
+      "size": [
+        287.86183776855466,
+        266
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 16
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 17
+        },
+        {
+          "localized_name": "control_net",
+          "name": "control_net",
+          "type": "CONTROL_NET",
+          "link": 18
+        },
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 19
+        },
+        {
+          "localized_name": "mask_optional",
+          "name": "mask_optional",
+          "shape": 7,
+          "type": "MASK",
+          "link": null
+        },
+        {
+          "localized_name": "timestep_kf",
+          "name": "timestep_kf",
+          "shape": 7,
+          "type": "TIMESTEP_KEYFRAME",
+          "link": null
+        },
+        {
+          "localized_name": "latent_kf_override",
+          "name": "latent_kf_override",
+          "shape": 7,
+          "type": "LATENT_KEYFRAME",
+          "link": null
+        },
+        {
+          "localized_name": "weights_override",
+          "name": "weights_override",
+          "shape": 7,
+          "type": "CONTROL_NET_WEIGHTS",
+          "link": null
+        },
+        {
+          "localized_name": "vae_optional",
+          "name": "vae_optional",
+          "shape": 7,
+          "type": "VAE",
+          "link": null
+        },
+        {
+          "localized_name": "strength",
+          "name": "strength",
+          "type": "FLOAT",
+          "widget": {
+            "name": "strength"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "start_percent",
+          "name": "start_percent",
+          "type": "FLOAT",
+          "widget": {
+            "name": "start_percent"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "end_percent",
+          "name": "end_percent",
+          "type": "FLOAT",
+          "widget": {
+            "name": "end_percent"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ]
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            22
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ACN_AdvancedControlNetApply_v2"
+      },
+      "widgets_values": [
+        1,
+        0,
+        1
+      ]
+    },
+    {
+      "id": 10,
+      "type": "KSampler",
+      "pos": [
+        1580,
+        110
+      ],
+      "size": [
+        270,
+        262
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "link": 20
+        },
+        {
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 21
+        },
+        {
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 22
+        },
+        {
+          "localized_name": "latent_image",
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 23
+        },
+        {
+          "localized_name": "seed",
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "steps",
+          "name": "steps",
+          "type": "INT",
+          "widget": {
+            "name": "steps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "cfg",
+          "name": "cfg",
+          "type": "FLOAT",
+          "widget": {
+            "name": "cfg"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "sampler_name",
+          "name": "sampler_name",
+          "type": "COMBO",
+          "widget": {
+            "name": "sampler_name"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "scheduler",
+          "name": "scheduler",
+          "type": "COMBO",
+          "widget": {
+            "name": "scheduler"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "denoise",
+          "name": "denoise",
+          "type": "FLOAT",
+          "widget": {
+            "name": "denoise"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            24
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        82000,
+        "fixed",
+        12,
+        1,
+        "euler",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 11,
+      "type": "VAEDecode",
+      "pos": [
+        1940,
+        140
+      ],
+      "size": [
+        140,
+        46
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "samples",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 24
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 25
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            26
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 12,
+      "type": "SaveImage",
+      "pos": [
+        2190,
+        110
+      ],
+      "size": [
+        270,
+        58
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 26
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "links": null
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "acn_anima_examples/any_pidinet_scribble"
+      ]
+    }
+  ],
+  "links": [
+    [
+      14,
+      2,
+      0,
+      3,
+      0,
+      "CLIP"
+    ],
+    [
+      15,
+      2,
+      0,
+      4,
+      0,
+      "CLIP"
+    ],
+    [
+      16,
+      3,
+      0,
+      9,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      17,
+      4,
+      0,
+      9,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      18,
+      8,
+      0,
+      9,
+      2,
+      "CONTROL_NET"
+    ],
+    [
+      19,
+      6,
+      0,
+      9,
+      3,
+      "IMAGE"
+    ],
+    [
+      20,
+      1,
+      0,
+      10,
+      0,
+      "MODEL"
+    ],
+    [
+      21,
+      9,
+      0,
+      10,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      22,
+      9,
+      1,
+      10,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      23,
+      5,
+      0,
+      10,
+      3,
+      "LATENT"
+    ],
+    [
+      24,
+      10,
+      0,
+      11,
+      0,
+      "LATENT"
+    ],
+    [
+      25,
+      7,
+      0,
+      11,
+      1,
+      "VAE"
+    ],
+    [
+      26,
+      11,
+      0,
+      12,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 20,
+      "title": "SHARED INPUTS",
+      "bounding": [
+        10,
+        35,
+        740,
+        900
+      ],
+      "color": "#3f789e",
+      "font_size": 32,
+      "flags": {}
+    },
+    {
+      "id": 21,
+      "title": "ANIMA LLLITE V2 ANY - PIDINET SCRIBBLE",
+      "bounding": [
+        810,
+        35,
+        1660,
+        900
+      ],
+      "color": "#6f4d86",
+      "font_size": 32,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 1,
+      "offset": [
+        0,
+        0
+      ]
+    },
+    "info": {
+      "name": "Anima LLLite v2 any - PiDiNet scribble",
+      "author": "Kosinkadink",
+      "description": "Advanced-ControlNet example for Anima LLLite v2 any with PiDiNet scribble conditioning."
+    }
+  },
+  "version": 0.4
+}

examples/qwen_image_inpainting/README.md

@@ -0,0 +1,83 @@
+# Qwen Image ControlNet inpainting
+
+This reviewer example adapts the active inpainting branch of ComfyUI's official
+Qwen Image workflow. It uses **Load Advanced ControlNet Model** and **Apply
+Advanced ControlNet**, while retaining the official Qwen base pipeline and the
+bypassed optional Lightning LoRA. Node titles are left at their ComfyUI
+defaults; the workflow stores no node title overrides.
+
+## Inputs and models
+
+Download the official inputs to `ComfyUI/input` with these exact names:
+
+- [`acn_qwen_inpaint_source.png`](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting/resolve/main/assets/images/image1.png)
+- [`acn_qwen_inpaint_mask.png`](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting/resolve/main/assets/masks/mask1.png)
+
+The model author's repository is
+[`InstantX/Qwen-Image-ControlNet-Inpainting`](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting).
+Download every model below to the listed folder under `ComfyUI/models`:
+
+| File and exact download | Folder |
+| --- | --- |
+| [`qwen_image_fp8_e4m3fn.safetensors`](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/diffusion_models/qwen_image_fp8_e4m3fn.safetensors) | `diffusion_models` |
+| [`qwen_2.5_vl_7b_fp8_scaled.safetensors`](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors) | `text_encoders` |
+| [`qwen_image_vae.safetensors`](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/vae/qwen_image_vae.safetensors) | `vae` |
+| [`Qwen-Image-InstantX-ControlNet-Inpainting.safetensors`](https://huggingface.co/Comfy-Org/Qwen-Image-InstantX-ControlNets/resolve/main/split_files/controlnet/Qwen-Image-InstantX-ControlNet-Inpainting.safetensors) | `controlnet` |
+| [`Qwen-Image-Lightning-4steps-V1.0.safetensors`](https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-4steps-V1.0.safetensors) | `loras` (optional and bypassed) |
+
+## Run
+
+1. Download the two inputs and five model files to the folders above.
+2. Load `qwen_image_inpainting.json` in ComfyUI.
+3. Queue the workflow unchanged.
+
+For command-line input reproduction:
+
+```sh
+curl -L https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting/resolve/main/assets/images/image1.png -o ComfyUI/input/acn_qwen_inpaint_source.png
+curl -L https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting/resolve/main/assets/masks/mask1.png -o ComfyUI/input/acn_qwen_inpaint_mask.png
+```
+
+The unchanged example uses seed `134554158057228` (fixed), 20 steps, CFG 2.5,
+Euler, the simple scheduler, denoise 1.0, model shift 3.1, control strength 1.0,
+and control start/end 0.0/1.0. Its prompt is `The Queen, on a throne,
+surrounded by Knights, HD, Realistic, Octane Render, Unreal engine`; the
+negative prompt is one space. The source is scaled with area interpolation to a
+maximum dimension of 1536. The optional 4-step LoRA remains bypassed; enabling
+it requires changing the sampler settings appropriately.
+
+The two native **Load Image** nodes are intentionally separate. **Image To
+Mask** reads the red channel of the mask PNG. That source `inpaint_mask` defines
+the region supplied to the inpainting ControlNet and the latent noise mask. It
+is not the Advanced-ControlNet effect mask. `effect_mask` is left unconnected
+and independently limits where control is injected. The Apply node also exposes
+unconnected timestep keyframe, latent keyframe, and weights ports for focused
+reviewer experiments.
+
+## Measured validation evidence
+
+These results were measured with fixed inputs and settings; they are recorded
+here rather than inferred from the example image:
+
+- A fresh isolated vanilla-versus-Advanced run had latent maximum/mean absolute
+  differences `0/0`, pixel maximum/mean differences `0/0`, and 0 changed
+  pixels.
+- An all-one effect mask exactly equaled unmasked Advanced output at latent and
+  pixel level. An all-zero effect mask exactly equaled no ControlNet at latent
+  and pixel level.
+- For right-half token-mask injection, relative to full control the left latent
+  mean delta was `0` and the right was `0.1332103`; relative to no control the
+  left was `0` and the right was `0.1835042`.
+- In a per-latent batch, the sample with strength 0 exactly equaled no control
+  at latent and pixel level.
+- Soft weights, timestep scheduling, and two-control stacking each executed
+  successfully.
+- The existing Anima real workflow rerun retained exact before/after latent and
+  pixel equality.
+
+Frontend and API validation confirms that the normal Apply node exposes the
+optional source mask as `inpaint_mask`. When connected to a ControlNet without
+source-mask support, that input is ignored and the normal control path is used.
+
+Workflow and result screenshots are linked from the PR instead of stored here
+to avoid repository growth.

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-advanced-controlnet"
 description = "Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks."
-version = "1.5.7"
+version = "1.6.0"
 license = { file = "LICENSE" }
 dependencies = []
 

tests/test_modern_control.py

@@ -0,0 +1,236 @@
+import os
+import sys
+import unittest
+from types import SimpleNamespace
+from unittest.mock import Mock, patch, sentinel
+
+comfyui_path = os.environ.get("COMFYUI_PATH")
+if comfyui_path:
+    sys.path.insert(0, comfyui_path)
+
+import torch
+
+from comfy.controlnet import T2IAdapter
+
+from adv_control.control import ControlNetAdvanced, T2IAdapterAdvanced
+from adv_control.nodes_main import AdvancedControlNetApply
+from adv_control.utils import ControlWeights
+
+
+class StopControlModel(Exception):
+    pass
+
+
+class ControlModel:
+    dtype = torch.float32
+
+    def __init__(self):
+        self.hint = None
+
+    def __call__(self, x, hint, timesteps, context, **kwargs):
+        self.hint = hint
+        raise StopControlModel
+
+
+class VideoVAE:
+    downscale_ratio = (4, 8, 8)
+
+    def __init__(self):
+        self.encoded_shape = None
+
+    def spacial_compression_encode(self):
+        return 8
+
+    def encode(self, image):
+        self.encoded_shape = image.shape
+        return torch.ones((image.shape[0], 4, 2, 2, 2))
+
+
+class ModernControlPreprocessingTests(unittest.TestCase):
+    def test_effect_mask_is_resized_to_qwen_tokens(self):
+        control = ControlNetAdvanced(ControlModel(), None)
+        control.x_noisy_shape = (1, 16, 4, 6)
+        control.mask_cond_hint = torch.tensor(
+            [[[[0.0, 0.0, 0.0, 1.0, 1.0, 1.0]] * 4]]
+        )
+        control.tk_mask_cond_hint = None
+        control.weights = SimpleNamespace(has_uncond_multiplier=False, has_uncond_mask=False)
+        control.latent_keyframes = None
+        control._current_timestep_keyframe = SimpleNamespace(strength=1.0)
+
+        output = torch.ones((1, 6, 4))
+        control.apply_advanced_strengths_and_masks(output, batched_number=1)
+
+        expected = torch.tensor(
+            [[[0.0] * 4, [0.5] * 4, [1.0] * 4, [0.0] * 4, [0.5] * 4, [1.0] * 4]]
+        )
+        torch.testing.assert_close(output, expected)
+
+    def test_effect_mask_matches_padded_flux_tokens_for_odd_latent_size(self):
+        control = ControlNetAdvanced(ControlModel(), None)
+        control.x_noisy_shape = (1, 16, 5, 7)
+        control.mask_cond_hint = torch.ones((1, 1, 5, 7))
+        control.tk_mask_cond_hint = None
+        control.weights = SimpleNamespace(has_uncond_multiplier=False, has_uncond_mask=False)
+        control.latent_keyframes = None
+        control._current_timestep_keyframe = SimpleNamespace(strength=1.0)
+
+        output = torch.ones((1, 12, 4))
+        control.apply_advanced_strengths_and_masks(output, batched_number=1)
+
+        torch.testing.assert_close(output, torch.ones_like(output))
+
+    def test_vae_compression_and_source_mask_match_5d_hint(self):
+        control_model = ControlModel()
+        vae = VideoVAE()
+        control = ControlNetAdvanced(control_model, None, compression_ratio=1, latent_format=SimpleNamespace(process_in=lambda value: value))
+        control.real_compression_ratio = 1
+        control.cond_hint_original = torch.ones((1, 3, 16, 16))
+        control.cond_hint = None
+        control.vae = vae
+        control.extra_concat_orig = [torch.zeros((1, 1, 16, 16))]
+        control.sub_idxs = None
+        control.model_sampling_current = SimpleNamespace(timestep=lambda value: value, calculate_input=lambda timestep, value: value)
+        control.prepare_mask_cond_hint = lambda **kwargs: None
+
+        with self.assertRaises(StopControlModel):
+            control.sliding_get_control(
+                torch.ones((1, 4, 2, 2, 2)),
+                torch.ones(1),
+                {"c_crossattn": torch.ones((1, 1, 1))},
+                1,
+                {},
+            )
+
+        self.assertEqual(tuple(vae.encoded_shape), (1, 16, 16, 3))
+        self.assertEqual(tuple(control_model.hint.shape), (1, 5, 2, 2, 2))
+
+
+class T2IAdapterTests(unittest.TestCase):
+    def test_effect_masks_are_applied_to_adapter_features(self):
+        control = T2IAdapterAdvanced(SimpleNamespace(), None, channels_in=3)
+        control.weights = ControlWeights.t2iadapter()
+        control.latent_keyframes = None
+        control.tk_mask_cond_hint = None
+        control._current_timestep_keyframe = SimpleNamespace(strength=1.0)
+
+        masks = {
+            "zero": torch.zeros((1, 1, 8, 8)),
+            "one": torch.ones((1, 1, 8, 8)),
+            "half": torch.cat((torch.zeros((1, 1, 8, 4)), torch.ones((1, 1, 8, 4))), dim=3),
+        }
+        for name, mask in masks.items():
+            with self.subTest(name=name):
+                features = torch.ones((1, 4, 8, 8))
+                control.mask_cond_hint = mask
+                control.apply_advanced_strengths_and_masks(features, batched_number=1)
+                torch.testing.assert_close(features, mask.expand_as(features))
+
+    def test_sliding_context_extends_single_hint_to_full_latent_length(self):
+        control = T2IAdapterAdvanced(SimpleNamespace(), None, channels_in=3)
+        original_hint = torch.ones((1, 3, 8, 8))
+        control.cond_hint_original = original_hint
+        control.cond_hint = None
+        control.sub_idxs = [2, 3]
+        control.full_latent_length = 4
+        control.prepare_mask_cond_hint = lambda **kwargs: None
+        selected_hint = None
+
+        def get_control(adapter, *args, **kwargs):
+            nonlocal selected_hint
+            selected_hint = adapter.cond_hint_original.clone()
+            return sentinel.output
+
+        with patch.object(T2IAdapter, "get_control", get_control):
+            result = control.get_control_advanced(
+                torch.ones((2, 4, 8, 8)),
+                torch.ones(2),
+                {},
+                1,
+                {},
+            )
+
+        self.assertIs(result, sentinel.output)
+        self.assertEqual(tuple(selected_hint.shape), (2, 3, 8, 8))
+        torch.testing.assert_close(selected_hint, original_hint.repeat(2, 1, 1, 1))
+        self.assertIs(control.cond_hint_original, original_hint)
+
+
+class AdvancedControlNetApplyTests(unittest.TestCase):
+    def apply_control(self, concat_mask, image, inpaint_mask, effect_mask=None):
+        control_net = SimpleNamespace(concat_mask=concat_mask, copy=Mock(return_value=sentinel.control_copy))
+        applied_control = SimpleNamespace(
+            allow_condhint_latents=False,
+            require_vae=False,
+            postpone_condhint_latents_check=False,
+            disarm=Mock(),
+            set_cond_hint=Mock(),
+            set_cond_hint_mask=Mock(),
+            set_previous_controlnet=Mock(),
+            verify_all_weights=Mock(),
+        )
+        applied_control.set_cond_hint.return_value = applied_control
+        positive = [[sentinel.positive_tensor, {}]]
+
+        with patch("adv_control.nodes_main.convert_to_advanced", return_value=applied_control), \
+             patch("adv_control.nodes_main.is_advanced_controlnet", return_value=True):
+            AdvancedControlNetApply.execute(
+                positive=positive,
+                negative=[],
+                control_net=control_net,
+                image=image,
+                strength=1.0,
+                start_percent=0.0,
+                end_percent=1.0,
+                mask_optional=effect_mask,
+                vae_optional=sentinel.vae,
+                inpaint_mask=inpaint_mask,
+            )
+
+        return applied_control
+
+    def test_all_zero_effect_mask_returns_original_conditioning(self):
+        positive = [[sentinel.positive_tensor, {"name": "positive"}]]
+        negative = [[sentinel.negative_tensor, {"name": "negative"}]]
+
+        result = AdvancedControlNetApply.execute(
+            positive=positive,
+            negative=negative,
+            control_net=sentinel.control_net,
+            image=torch.ones((1, 8, 8, 3)),
+            strength=1.0,
+            start_percent=0.0,
+            end_percent=1.0,
+            mask_optional=torch.zeros((1, 8, 8)),
+        )
+
+        self.assertIs(result.args[0], positive)
+        self.assertIs(result.args[1], negative)
+
+    def test_source_mask_and_effect_mask_stay_independent(self):
+        image = torch.ones((1, 2, 2, 3))
+        inpaint_mask = torch.tensor([[[1.0, 0.0], [1.0, 0.0]]])
+        effect_mask = torch.full((1, 2, 2), 0.25)
+        applied_control = self.apply_control(True, image, inpaint_mask, effect_mask)
+
+        inputs = applied_control.set_cond_hint.call_args.args
+        source_mask = 1.0 - inpaint_mask.unsqueeze(1)
+        torch.testing.assert_close(inputs[0], (image * source_mask.movedim(1, -1)).movedim(-1, 1))
+        torch.testing.assert_close(inputs[4][0], source_mask)
+        torch.testing.assert_close(applied_control.set_cond_hint_mask.call_args.args[0], effect_mask)
+
+    def test_inpaint_mask_is_ignored_for_other_controlnets(self):
+        image = torch.ones((1, 2, 2, 3))
+        inpaint_mask = torch.tensor([[[1.0, 0.0], [1.0, 0.0]]])
+        applied_control = self.apply_control(False, image, inpaint_mask)
+
+        inputs = applied_control.set_cond_hint.call_args.args
+        torch.testing.assert_close(
+            inputs[0],
+            image.movedim(-1, 1),
+        )
+        self.assertEqual(inputs[4], [])
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_regressions.py

@@ -0,0 +1,104 @@
+import os
+import sys
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+comfyui_path = os.environ.get("COMFYUI_PATH")
+if comfyui_path:
+    sys.path.insert(0, comfyui_path)
+
+import torch
+
+from comfy.controlnet import T2IAdapter
+
+from adv_control.control import T2IAdapterAdvanced
+from adv_control.control_lllite import LLLiteModule
+from adv_control.control_reference import REF_CONTROL_LIST_ALL, RefConst, refcn_diffusion_model_wrapper_factory
+
+
+class LLLiteRegressionTests(unittest.TestCase):
+    def create_module(self):
+        torch.manual_seed(1)
+        return LLLiteModule("test", False, 2, 1, 2, 2)
+
+    def create_control(self, effect_mask=None, timestep_mask=None, uncond_multiplier=1.0):
+        return SimpleNamespace(
+            sub_idxs=None,
+            cond_hint=torch.ones((1, 3, 8, 8)),
+            latent_dims_div2=None,
+            latent_dims_div4=None,
+            mask_cond_hint=effect_mask,
+            tk_mask_cond_hint=timestep_mask,
+            latent_keyframes=None,
+            weights=SimpleNamespace(
+                has_uncond_multiplier=uncond_multiplier != 1.0,
+                uncond_multiplier=uncond_multiplier,
+            ),
+            batched_number=2,
+            cond_or_uncond=[0, 1],
+            strength=1.0,
+            _current_timestep_keyframe=SimpleNamespace(strength=1.0),
+        )
+
+    def test_unconditional_multiplier_uses_sampling_condition_order(self):
+        control = self.create_control(uncond_multiplier=0.25)
+        output = self.create_module()(torch.ones((2, 1, 2)), control)
+
+        torch.testing.assert_close(output[1], output[0] * 0.25)
+
+    def test_timestep_mask_applies_without_effect_mask(self):
+        control = self.create_control(timestep_mask=torch.zeros((1, 8, 8)))
+        output = self.create_module()(torch.ones((2, 1, 2)), control)
+
+        torch.testing.assert_close(output, torch.zeros_like(output))
+
+    def test_effect_and_timestep_masks_are_combined(self):
+        control = self.create_control(
+            effect_mask=torch.ones((1, 8, 8)),
+            timestep_mask=torch.zeros((1, 8, 8)),
+        )
+        output = self.create_module()(torch.ones((2, 1, 2)), control)
+
+        torch.testing.assert_close(output, torch.zeros_like(output))
+
+
+class T2IAdapterRegressionTests(unittest.TestCase):
+    def test_sliding_context_extends_hint_to_full_latent_length(self):
+        adapter = object.__new__(T2IAdapterAdvanced)
+        adapter.sub_idxs = [2, 3]
+        adapter.full_latent_length = 4
+        adapter.cond_hint_original = torch.tensor([[[[7.0]]]])
+        adapter.cond_hint = None
+        adapter.prepare_mask_cond_hint = lambda **kwargs: None
+
+        with patch.object(T2IAdapter, "get_control", lambda self, *args, **kwargs: self.cond_hint_original.clone()):
+            output = adapter.get_control_advanced(torch.empty((2, 4, 1, 1)), None, None, 1, {})
+
+        self.assertEqual(output.flatten().tolist(), [7.0, 7.0])
+        self.assertEqual(adapter.cond_hint_original.flatten().tolist(), [7.0])
+
+
+class ReferenceRegressionTests(unittest.TestCase):
+    def test_cleanup_does_not_hide_original_exception(self):
+        class ReferenceInjections:
+            cleaned = False
+
+            def clean_ref_module_mem(self):
+                self.cleaned = True
+
+        reference_injections = ReferenceInjections()
+        wrapper = refcn_diffusion_model_wrapper_factory(reference_injections)
+        transformer_options = {
+            REF_CONTROL_LIST_ALL: [SimpleNamespace(should_run=lambda: True)],
+            RefConst.REFCN_PRESENT_IN_CONDS: True,
+        }
+
+        with self.assertRaisesRegex(KeyError, "cond_or_uncond"):
+            wrapper(lambda *args, **kwargs: None, torch.zeros(1), None, None, None, None, transformer_options)
+
+        self.assertTrue(reference_injections.cleaned)
+
+
+if __name__ == "__main__":
+    unittest.main()

web/js/autosize.js

@@ -1,53 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function addResizeHook(node, padding, useOldMin=false) {
-    let origOnCreated = node.onNodeCreated
-    node.onNodeCreated = function() {
-        let r = origOnCreated?.apply(this, arguments)
-        let size = this.computeSize();
-        size[0] += padding || 0;
-        if (useOldMin) {
-            //equal to LiteGraph.NODE_WIDTH*1.5*1.5
-            size[0] = Math.max(size[0], 315)
-        }
-        this.setSize(size);
-        return r
-    }
-}
-
-app.registerExtension({
-    name: "AdvancedControlNet.autosize",
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        //since python_module is based off folder path,
-        //it could be changed by users and should only be used as fallback
-        if (nodeData?.name?.startsWith("ACN_")
-            || nodeData.python_module == 'custom_nodes.ComfyUI-Advanced-ControlNet') {
-            if (nodeData?.input?.hidden?.autosize) {
-                addResizeHook(nodeType.prototype, nodeData.input.hidden.autosize[1]?.padding)
-            } else if (!nodeData?.input?.optional?.autosize) {
-                addResizeHook(nodeType.prototype, 0, true)
-            }
-        }
-    },
-    async getCustomWidgets() {
-        return {
-            ACNAUTOSIZE(node, inputName, inputData) {
-                let w = {
-                    name : inputName,
-                    type : "ACN.AUTOSIZE",
-                    value : "",
-                    options : {"serialize": false},
-                    computeSize : function(width) {
-                        return [0, -4];
-                    }
-                }
-                if (!node.widgets) {
-                    node.widgets = []
-                }
-                node.widgets.push(w)
-                addResizeHook(node, inputData[1].padding);
-                return w;
-            }
-        }
-    }
-});

web/js/documentation.js

@@ -1,293 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function chainCallback(object, property, callback) {
-    if (object == undefined) {
-        //This should not happen.
-        console.error("Tried to add callback to non-existant object")
-        return;
-    }
-    if (property in object && object[property]) {
-        const callback_orig = object[property]
-        object[property] = function () {
-            const r = callback_orig.apply(this, arguments);
-            callback.apply(this, arguments);
-            return r
-        };
-    } else {
-        object[property] = callback;
-    }
-}
-var helpDOM;
-function initHelpDOM() {
-    let parentDOM = document.createElement("div");
-    document.body.appendChild(parentDOM)
-    parentDOM.appendChild(helpDOM)
-    helpDOM.className = "litegraph";
-    let scrollbarStyle = document.createElement('style');
-    scrollbarStyle.innerHTML = `
-    <style id="scroll-properties">
-            * {
-                scrollbar-width: 6px;
-                scrollbar-color: #0003  #0000;
-            }
-            ::-webkit-scrollbar {
-                background: transparent;
-                width: 6px;
-            }
-            ::-webkit-scrollbar-thumb {
-                background: #0005;
-                border-radius: 20px
-            }
-            ::-webkit-scrollbar-button {
-                display: none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-mute-button {
-                display:none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-fullscreen-button {
-                display:none;
-            }
-        </style>
-    `
-    parentDOM.appendChild(scrollbarStyle)
-    chainCallback(app.canvas, "onDrawForeground", function (ctx, visible_rect){
-        let n = helpDOM.node
-        if (!n || !n?.graph) {
-            parentDOM.style['left'] = '-5000px'
-            return
-        }
-        //draw : function(ctx, node, widgetWidth, widgetY, height) {
-        //update widget position, even if off screen
-        const transform = ctx.getTransform();
-        const scale = app.canvas.ds.scale;//gets the litegraph zoom
-        //calculate coordinates with account for browser zoom
-        const bcr = app.canvas.canvas.getBoundingClientRect()
-        const x = transform.e*scale/transform.a + bcr.x;
-        const y = transform.f*scale/transform.a + bcr.y;
-        //TODO: text reflows at low zoom. investigate alternatives
-        Object.assign(parentDOM.style, {
-            left: (x+(n.pos[0] + n.size[0]+15)*scale) + "px",
-            top: (y+(n.pos[1]-LiteGraph.NODE_TITLE_HEIGHT)*scale) + "px",
-            width: "400px",
-            minHeight: "100px",
-            maxHeight: "600px",
-            overflowY: 'scroll',
-            transformOrigin: '0 0',
-            transform: 'scale(' + scale + ',' + scale +')',
-            fontSize: '18px',
-            backgroundColor: LiteGraph.NODE_DEFAULT_BGCOLOR,
-            boxShadow: '0 0 10px black',
-            borderRadius: '4px',
-            padding: '3px',
-            zIndex: 3,
-            position: "absolute",
-            display: 'inline',
-        });
-    });
-    function setCollapse(el, doCollapse) {
-        if (doCollapse) {
-            el.children[0].children[0].innerHTML = '+'
-            Object.assign(el.children[1].style, {
-                color: '#CCC',
-                overflowX: 'hidden',
-                width: '0px',
-                minWidth: 'calc(100% - 20px)',
-                textOverflow: 'ellipsis',
-                whiteSpace: 'nowrap',
-            })
-            for (let child of el.children[1].children) {
-                if (child.style.display != 'none'){
-                    child.origDisplay = child.style.display
-                }
-                child.style.display = 'none'
-            }
-        } else {
-            el.children[0].children[0].innerHTML = '-'
-            Object.assign(el.children[1].style, {
-                color: '',
-                overflowX: '',
-                width: '100%',
-                minWidth: '',
-                textOverflow: '',
-                whiteSpace: '',
-            })
-            for (let child of el.children[1].children) {
-                child.style.display = child.origDisplay
-            }
-        }
-    }
-    helpDOM.collapseOnClick = function() {
-        let doCollapse = this.children[0].innerHTML == '-'
-        setCollapse(this.parentElement, doCollapse)
-    }
-    helpDOM.selectHelp = function(name, value) {
-        //attempt to navigate to name in help
-        function collapseUnlessMatch(items,t) {
-            var match = items.querySelector('[vhs_title="' + t + '"]')
-            if (!match) {
-                for (let i of items.children) {
-                    if (i.innerHTML.slice(0,t.length+5).includes(t)) {
-                        match = i
-                        break
-                    }
-                }
-            }
-            if (!match) {
-                return null
-            }
-            //For longer documentation items with fewer collapsable elements,
-            //scroll to make sure the entirety of the selected item is visible
-            //This has the unfortunate side effect of trying to scroll the main
-            //window if the documentation windows is forcibly offscreen,
-            //but it's easy to simply scroll the main window back and seems to
-            //have no visual side effects
-            match.scrollIntoView(false)
-            window.scrollTo(0,0)
-            for (let i of items.querySelectorAll('.VHS_collapse')) {
-                if (i.contains(match)) {
-                    setCollapse(i, false)
-                } else {
-                    setCollapse(i, true)
-                }
-            }
-            return match
-        }
-        let target = collapseUnlessMatch(helpDOM, name)
-        if (target && value) {
-            collapseUnlessMatch(target, value)
-        }
-    }
-
-    helpDOM.addHelp = function(node, nodeType, description) {
-        if (!description) {
-            return
-        }
-        //Pad computed size for the clickable question mark
-        let originalComputeSize = node.computeSize
-        node.computeSize = function() {
-            let size = originalComputeSize.apply(this, arguments)
-            if (!this.title) {
-                return size
-            }
-            let title_width = this.title.length * 0.6 * LiteGraph.NODE_TEXT_SIZE
-            size[0] = Math.max(size[0], title_width + LiteGraph.NODE_TITLE_HEIGHT)
-            return size
-        }
-
-        node.description = description
-        chainCallback(node, "onDrawForeground", function (ctx) {
-            //draw question mark
-            ctx.save()
-            ctx.font = 'bold 20px Arial'
-            ctx.fillText("?", this.size[0]-17, -8)
-            ctx.restore()
-        })
-        chainCallback(node, "onMouseDown", function (e, pos, canvas) {
-            //On click would be preferred, but this'll be good enough
-            if (pos[1] < 0 && pos[0] + LiteGraph.NODE_TITLE_HEIGHT > this.size[0]) {
-                //corner question mark clicked
-                if (helpDOM.node == this) {
-                    helpDOM.node = undefined
-                } else {
-                    helpDOM.node = this;
-                    helpDOM.innerHTML = this.description || "no help provided ".repeat(20)
-                    for (let e of helpDOM.querySelectorAll('.VHS_collapse')) {
-                        e.children[0].onclick = helpDOM.collapseOnClick
-                        e.children[0].style.cursor = 'pointer'
-                    }
-                    for (let e of helpDOM.querySelectorAll('.VHS_precollapse')) {
-                        setCollapse(e, true)
-                    }
-                }
-                return true
-            }
-        })
-        let timeout = null
-        chainCallback(node, "onMouseMove", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-            if (helpDOM.node != this) {
-                return
-            }
-            timeout = setTimeout(() => {
-                let n = this
-                if (pos[0] > 0 && pos[0] < n.size[0]
-                    && pos[1] > 0 && pos[1] < n.size[1]) {
-                    //TODO: provide help specific to element clicked
-                    let inputRows = Math.max(n.inputs.length, n.outputs.length)
-                    if (pos[1] < LiteGraph.NODE_SLOT_HEIGHT * inputRows) {
-                        let row = Math.floor((pos[1] - 7) / LiteGraph.NODE_SLOT_HEIGHT)
-                        if (pos[0] < n.size[0]/2) {
-                            if (row < n.inputs.length) {
-                                helpDOM.selectHelp(n.inputs[row].name)
-                            }
-                        } else {
-                            if (row < n.outputs.length) {
-                                helpDOM.selectHelp(n.outputs[row].name)
-                            }
-                        }
-                    } else {
-                        //probably widget, but widgets have variable height.
-                        let basey = LiteGraph.NODE_SLOT_HEIGHT * inputRows + 6
-                        for (let w of n.widgets) {
-                            if (w.y) {
-                                basey = w.y
-                            }
-                            let wheight = LiteGraph.NODE_WIDGET_HEIGHT+4
-                            if (w.computeSize) {
-                                wheight = w.computeSize(n.size[0])[1]
-                            }
-                            if (pos[1] < basey + wheight) {
-                                helpDOM.selectHelp(w.name, w.value)
-                                break
-                            }
-                            basey += wheight
-                        }
-                    }
-                }
-            }, 500)
-        })
-        chainCallback(node, "onMouseLeave", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-        });
-    }
-}
-
-
-
-app.registerExtension({
-    name: "AdvancedControlNet.documentation",
-    async init() {
-        if (app.VHSHelp) {
-            helpDOM = app.VHSHelp
-        } else {
-            helpDOM = document.createElement("div");
-            initHelpDOM()
-            app.VHSHelp = helpDOM
-        }
-    },
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        // NOTE: May need manual adjusting for the few non-namespaced nodes
-        if(nodeData?.name?.startsWith("ACN_") && nodeData.description) {
-            let description = nodeData.description
-            let el = document.createElement("div")
-            el.innerHTML = description
-            if (!el.children.length) {
-                //Is plaintext. Do minor convenience formatting
-                let chunks = description.split('\n')
-                nodeData.description = chunks[0]
-                description = chunks.join('<br>')
-            } else {
-                nodeData.description = el.querySelector('#VHS_shortdesc')?.innerHTML || el.children[1]?.firstChild?.innerHTML
-            }
-            chainCallback(nodeType.prototype, "onNodeCreated", function () {
-                helpDOM.addHelp(this, nodeType, description)
-            })
-        }
-    },
-});